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Paper Citation Record · LEDGER

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields

As of 3 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 0 inbound Pith citation observations for arXiv:2509.26005.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2509.26005 v4

Coverage vector

measured 53 of 53 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-22T12:43:18.499079Z

measured 53 of 53 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

53 of 53 outbound references displayed

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External citation measurements

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Outbound references

Observation 5921d8a3-dcd9-4e3b-b17c-4ddbcf47b5c1 · outbound

This paper cites Gaussian processes with linear operator inequality constraints.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Gaussian processes with linear operator inequality constraints

Reference 1

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Observation 7b41fb0e-488d-4851-b2d5-be00c67feb04 · outbound

This paper cites Kernels for vector-valued functions: A review.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Kernels for vector-valued functions: A review

Reference 2

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Observation 20553035-a1a5-4166-995f-5d0064ec242c · outbound

This paper cites A Bayesian approach to Lagrangian data assimilation.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields A Bayesian approach to Lagrangian data assimilation

Reference 3

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Observation 55fee479-6622-439c-bc8a-a03da1820600 · outbound

This paper cites Gaussian processes at the Helm (holtz) a more fluid model for ocean currents.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Gaussian processes at the Helm (holtz) a more fluid model for ocean currents

Reference 4

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Observation ce52307a-85bf-46ae-acfa-51a39e333ad2 · outbound

This paper cites The Helmholtz-Hodge decomposition—a survey.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields The Helmholtz-Hodge decomposition—a survey

Reference 5

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Observation 95014442-9d19-41e3-848b-32a15593cd09 · outbound

This paper cites A causation-based computationally efficient strategy for deploying Lagrangian drifters to improve real-time state estimation.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields A causation-based computationally efficient strategy for deploying Lagrangian drifters to improve real-time state estimation

Reference 6

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Observation d9313808-314d-4109-a12a-7d865dd2a6cd · outbound

This paper cites Lagrangian descriptors with uncertainty.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Lagrangian descriptors with uncertainty

Reference 7

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Observation 5657cca3-8383-447c-92fd-c4558d9b1d3c · outbound

This paper cites Launching drifter observations in the presence of uncertainty.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Launching drifter observations in the presence of uncertainty

Reference 8

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Observation d9885737-88c9-48de-8907-a98279a441a2 · outbound

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BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Unresolved cited work

Reference 9

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Observation 19b1d588-01a0-41cb-91a0-79903f554270 · outbound

This paper cites Ocean circulation kinetic energy: Reservoirs, sources, and sinks.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Ocean circulation kinetic energy: Reservoirs, sources, and sinks

Reference 10

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Observation a24b54c5-25e2-4246-b950-d89b122e8a00 · outbound

This paper cites Deep adaptive design: Amortizing sequential Bayesian experimental design.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Deep adaptive design: Amortizing sequential Bayesian experimental design

Reference 11

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Observation 4ef21d0c-c5c8-46d2-adaf-584bd3828b2a · outbound

This paper cites An unstructured-grid, finite-volume, nonhydrostatic, parallel coastal ocean simulator.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields An unstructured-grid, finite-volume, nonhydrostatic, parallel coastal ocean simulator

Reference 12

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Observation 52fb66df-9038-45c2-8811-285b16567668 · outbound

This paper cites Surrogates: Gaussian Process Modeling, Design, and Optimization for the Applied Sciences.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Surrogates: Gaussian Process Modeling, Design, and Optimization for the Applied Sciences

Reference 13

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Observation d54f2e04-a67c-4ba4-8104-2b4d01d621f2 · outbound

This paper cites Lagrangian Analysis and Prediction of Coastal and Ocean Dynamics.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Lagrangian Analysis and Prediction of Coastal and Ocean Dynamics

Reference 14

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Observation 98affb46-0fd5-4086-b079-bec65eb19e5e · outbound

This paper cites Spatio-temporal variational Gaussian processes.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Spatio-temporal variational Gaussian processes

Reference 15

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Observation 53aafdb6-083e-4bbf-b066-5005a695e33d · outbound

This paper cites Physics-Informed Variational State-Space Gaussian Processes.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Physics-Informed Variational State-Space Gaussian Processes

Reference 16

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Observation 1371aa5d-1220-475e-bcb6-778e51b2af7c · outbound

This paper cites Kalman filtering and smoothing solutions to temporal Gaussian process regression models.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Kalman filtering and smoothing solutions to temporal Gaussian process regression models

Reference 17

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Observation f2cd126d-7124-4507-a2e6-48ece58f9a18 · outbound

This paper cites Nesting particle filters for experimental design in dynamical systems.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Nesting particle filters for experimental design in dynamical systems

Reference 18

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Observation aa068aad-7f21-4597-b1e9-b7f455bfc62a · outbound

This paper cites Oil spill modeling: A critical review on current trends, perspectives, and challenges.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Oil spill modeling: A critical review on current trends, perspectives, and challenges

Reference 19

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Observation f9fe8c2b-4090-4542-8a97-c836dbf5aa6b · outbound

This paper cites Robust and Conjugate Spatio-Temporal Gaussian Processes.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Robust and Conjugate Spatio-Temporal Gaussian Processes

Reference 20

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Observation fb5dc7c0-bee9-4df6-b9c5-2509762142a3 · outbound

This paper cites A gridded surface current product for the Gulf of Mexico from consolidated drifter measurements.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields A gridded surface current product for the Gulf of Mexico from consolidated drifter measurements

Reference 21

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Observation 434a6a1c-376b-4c6d-aebd-0b1bc8abe325 · outbound

This paper cites Fractional Brownian motion, the Mat \'e rn process, and stochastic modeling of turbulent dispersion.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Fractional Brownian motion, the Mat \'e rn process, and stochastic modeling of turbulent dispersion

Reference 22

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Observation 69b34bbf-f138-4255-86ba-b0bf67347475 · outbound

This paper cites An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach

Reference 23

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This paper cites The SPDE approach for Gaussian and non-Gaussian fields: 10 years and still running.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields The SPDE approach for Gaussian and non-Gaussian fields: 10 years and still running

Reference 24

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BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields On a measure of the information provided by an experiment

Reference 25

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Observation 1486e3cd-40c5-4dc7-8f7a-14a75934db49 · outbound

This paper cites Advances in the application of surface drifters.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Advances in the application of surface drifters

Reference 26

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This paper cites Lagrangian descriptors: A method for revealing phase space structures of general time dependent dynamical systems.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Lagrangian descriptors: A method for revealing phase space structures of general time dependent dynamical systems

Reference 27

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This paper cites GPJax: A Gaussian Process Framework in JAX.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields GPJax: A Gaussian Process Framework in JAX

Reference 28

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BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Drifter launch strategies based on Lagrangian templates

Reference 29

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Observation ceb81a65-d34e-4578-8624-8429d896773c · outbound

This paper cites Inferring flow energy, space scales, and timescales: freely drifting vs.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Inferring flow energy, space scales, and timescales: freely drifting vs

Reference 30

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Observation 3c5fbb2f-379d-459c-848e-6cd9e4c6bdce · outbound

This paper cites Modern Bayesian experimental design.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Modern Bayesian experimental design

Reference 31

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Observation ef65bb99-89be-400a-9c6f-13dfc5f4b3f6 · outbound

This paper cites A seasonal harmonic model for internal tide amplitude prediction.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields A seasonal harmonic model for internal tide amplitude prediction

Reference 32

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Observation 25b0a6c3-6e9e-47ee-89d7-61b457a0a982 · outbound

This paper cites Monte Carlo Statistical Methods , volume 2.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Monte Carlo Statistical Methods , volume 2

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-05-22T12:44:52.917277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=arxiv_source observed=2026-05-22T12:43:18.499079Z digest=sha256:7320739d342ea89b7fd64b81983ae792e7432911a94728bc24252b861aae4472

Observation 0862bc25-ba25-4336-ba18-d7d23138252e · outbound

This paper cites Gaussian Markov Random Fields: Theory and Applications.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Gaussian Markov Random Fields: Theory and Applications

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-05-22T12:44:52.908154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=arxiv_source observed=2026-05-22T12:43:18.499079Z digest=sha256:ddbb92330ddfd24ae6e361c92f2cc3c4c3ab4562bb225d3886c66a0042fb3f40

Observation a04d812b-4f54-4a4f-9c67-04b018d8c565 · outbound

This paper cites A review of modern computational algorithms for Bayesian optimal design.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields A review of modern computational algorithms for Bayesian optimal design

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-05-22T12:44:52.849153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=arxiv_source observed=2026-05-22T12:43:18.499079Z digest=sha256:3e88c26dccae0b0259c53de76f34414570098b25fbe9f35cf4c937b395ee2ef3

Observation 79e59a05-713e-45a6-8a9c-2457d328f957 · outbound

This paper cites Scalable inference for structured Gaussian process models.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Scalable inference for structured Gaussian process models

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-05-22T12:44:52.904743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=arxiv_source observed=2026-05-22T12:43:18.499079Z digest=sha256:fede5ba518ce5f89dc3bb68d093de267ce333ed11ade7d50d564f539d227c001

Observation 9a95917b-1e75-409a-8366-0b1c215342a5 · outbound

This paper cites Using flow geometry for drifter deployment in Lagrangian data assimilation.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Using flow geometry for drifter deployment in Lagrangian data assimilation

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-05-22T12:44:52.901561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=arxiv_source observed=2026-05-22T12:43:18.499079Z digest=sha256:4ff83403f17819c0f38b7e0f561b7e74d5b0f0b0907e1752a204be1555d9cd59

Observation ebbe38cb-61e1-4044-acb8-e32ec72f38b6 · outbound

This paper cites Applied Stochastic Differential Equations , volume 10.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Applied Stochastic Differential Equations , volume 10

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-05-22T12:44:52.898036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=arxiv_source observed=2026-05-22T12:43:18.499079Z digest=sha256:567449ae2ee5b4f68847409b0fd7077d191f1e7e936e46446b97f33ff5d7f8a9

Observation 37dbc13e-13ba-48f7-84ef-41a0d0e2e8a5 · outbound

This paper cites Spatiotemporal learning via infinite-dimensional Bayesian filtering and smoothing: A look at Gaussian process regression through Kalman filtering.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Spatiotemporal learning via infinite-dimensional Bayesian filtering and smoothing: A look at Gaussian process regression through Kalman filtering

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-05-22T12:44:52.893273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=arxiv_source observed=2026-05-22T12:43:18.499079Z digest=sha256:6cf76834d0c79bee47888e6dccaba9da78ca9727544e122ad84d06683c174d2c

Observation fa536cff-0448-42db-bbc4-b8c6b4188b90 · outbound

This paper cites Active learning literature survey.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Active learning literature survey

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-05-22T12:44:52.889811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=arxiv_source observed=2026-05-22T12:43:18.499079Z digest=sha256:2adb837e0840372565b47735b1a42ab8cd7349bb8f611d1702606f48fc490d21

Observation 3a9077dd-4dac-4fc3-9c0b-6d65c2a10e5d · outbound

This paper cites Prediction-Centric Uncertainty Quantification via MMD.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Prediction-Centric Uncertainty Quantification via MMD

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-05-22T12:44:52.886570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=arxiv_source observed=2026-05-22T12:43:18.499079Z digest=sha256:7520ac9e76de678c26210e3f87c90f60722f57ca99a17c674fe12daf481da770

Observation 360ab29e-8c6c-44af-a040-a4959c261e9b · outbound

This paper cites Stochastic differential equation methods for spatio-temporal Gaussian process regression.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Stochastic differential equation methods for spatio-temporal Gaussian process regression

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-05-22T12:44:52.882656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=arxiv_source observed=2026-05-22T12:43:18.499079Z digest=sha256:281710cdbfd065a60435a0320cc34f76e8059e4ae2eda9fb0104dd2f08ba7569

Observation b69b1d89-0fc1-4561-aa5f-7284c5fe3cb0 · outbound

This paper cites Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-05-22T12:44:52.839601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=arxiv_source observed=2026-05-22T12:43:18.499079Z digest=sha256:e0bc47599cb00a8464ffefeb7d981978ac9ac6ced23948dd8a8e6b518cf49eae

Observation 2d53baa1-9cfe-4d68-b9d4-63de5b0c3990 · outbound

This paper cites An Introduction to Numerical Analysis.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields An Introduction to Numerical Analysis

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-05-22T12:44:52.879125Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=arxiv_source observed=2026-05-22T12:43:18.499079Z digest=sha256:be8a264f186d09d5d4fd692c94cbf38bf0ab393b5e4d3d988028c2f48f9fde04

Observation ee08c1a2-dc40-4839-b95f-8a665706146b · outbound

This paper cites Numerical Linear Algebra , volume 50.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Numerical Linear Algebra , volume 50

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-05-22T12:44:52.875985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=arxiv_source observed=2026-05-22T12:43:18.499079Z digest=sha256:983b9abb086bf9d79741a542ddd5ccd6c67334412c9bf9bb9484a9e3b3f387ef

Observation 93800895-1865-43c1-87da-5d3aaa34a612 · outbound

This paper cites An efficient drifters deployment strategy to evaluate water current velocity fields.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields An efficient drifters deployment strategy to evaluate water current velocity fields

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-05-22T12:44:52.872379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=arxiv_source observed=2026-05-22T12:43:18.499079Z digest=sha256:a7133076f4349c0a68290426b26692e03fd422f64956d1b8d8f02adb008de913

Observation 03ec158e-e34c-4b85-b737-fef98f112f1a · outbound

This paper cites Dispersion of surface drifters in the tropical Atlantic.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Dispersion of surface drifters in the tropical Atlantic

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-05-22T12:44:52.845698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=arxiv_source observed=2026-05-22T12:43:18.499079Z digest=sha256:4648999c7894f559e4070a6fc90b6eb82bbd2febfcc47195488a704daff91b68

Observation f0eaf690-95bf-4de8-b0ab-952f661c6387 · outbound

This paper cites Nearshore internal bores and turbulent mixing in southern Monterey Bay.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Nearshore internal bores and turbulent mixing in southern Monterey Bay

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-05-22T12:44:52.874165Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=arxiv_source observed=2026-05-22T12:43:18.499079Z digest=sha256:7093e25f4d5e7361ab204941c039c44af813a128f946200245b0b714a7fb547c

Observation adb53b6f-c8d1-4cd4-8d5c-78626a216304 · outbound

This paper cites On stationary processes in the plane.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields On stationary processes in the plane

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-05-22T12:44:52.864790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=arxiv_source observed=2026-05-22T12:43:18.499079Z digest=sha256:0a80ff0acb9e73a84fc1892dca99867efb9f25531c4203b26a883a1a48f81552

Observation 2098f280-6c3f-4287-91e3-7d71969c8229 · outbound

This paper cites Stochastic processes in several dimensions.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Stochastic processes in several dimensions

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-05-22T12:44:52.860468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=arxiv_source observed=2026-05-22T12:43:18.499079Z digest=sha256:4da60c9fdf1ea74cf5ace1d7caf0ec34c66d054a9774c64a14bc18555607920f

Observation d2602f0e-a9c4-4b8a-8cb9-90841271f7e4 · outbound

This paper cites Gaussian Processes for Machine Learning , volume 2.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Gaussian Processes for Machine Learning , volume 2

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-05-22T12:44:52.842796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=arxiv_source observed=2026-05-22T12:43:18.499079Z digest=sha256:2ac2bad4b53979483510d2c8a3061b672bd5914a923669c40a02f7b25b793dc7

Observation ca9d89ea-2f4a-4363-aafe-8ec286729931 · outbound

This paper cites HHD-GP: Incorporating Helmholtz-Hodge Decomposition into Gaussian Processes for Learning Dynamical Systems.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields HHD-GP: Incorporating Helmholtz-Hodge Decomposition into Gaussian Processes for Learning Dynamical Systems

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-05-22T12:44:52.856461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=arxiv_source observed=2026-05-22T12:43:18.499079Z digest=sha256:3478140a8337ba1342c972169fe7532d39478e242d4597de974f67a1c957f922

Observation 1f178c85-2fec-407d-8fcb-edb39499c7c1 · outbound

This paper cites BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-05-22T12:44:52.852859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=arxiv_source observed=2026-05-22T12:43:18.499079Z digest=sha256:cc3fa7fdd92eaad4ac04165c7b65921b7f2c3d2dc398fc0a712e4bfa307754e2

Pith citing papers

No inbound Pith citation observations are available.