Paper Citation Record · LEDGER

A multi-scale loss formulation for learning a probabilistic model with proper score optimisation

As of 19 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 2 inbound Pith citation observations for arXiv:2506.10868.

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

pith.paper-citation-record.v1
2506.10868 v1

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:20:19.780671Z

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T10:37:41.543599Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

17 of 17 outbound references displayed

verified exact2
verified fuzzy2
unresolved13
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b6834b4b-b1d5-4283-a4e0-8611dc6fa56f · outbound

This paper cites GenCast: Diffusion-based ensemble forecasting for medium-range weather.

A multi-scale loss formulation for learning a probabilistic model with proper score optimisation GenCast: Diffusion-based ensemble forecasting for medium-range weather

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T04:20:19.768521Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 03abe5c6-d6e0-49c1-8994-6a307cf76845 · outbound

This paper cites doi: 10.1029/2023ms004177.

A multi-scale loss formulation for learning a probabilistic model with proper score optimisation doi: 10.1029/2023ms004177

Reference 14

Resolution: verified exact; doi, observed 2026-08-07T04:20:19.808964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation caf6a60e-d262-410a-9cb7-5bcbfabf14f0 · outbound

This paper cites an unresolved cited work.

A multi-scale loss formulation for learning a probabilistic model with proper score optimisation Unresolved cited work

Reference 16

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:20:19.991323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation abb9df3e-fb68-4554-81be-6073ea0c7884 · outbound

This paper cites Denoising Diffusion Probabilistic Models.

A multi-scale loss formulation for learning a probabilistic model with proper score optimisation Denoising Diffusion Probabilistic Models

Reference 2000

Resolution: unresolved; no resolver link, observed 2026-08-07T04:20:19.603642Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4d5b0b7-4059-4489-91ce-6ff0d8ae1fbe · outbound

This paper cites Diffusion is spectral autoregression.https://sander.

A multi-scale loss formulation for learning a probabilistic model with proper score optimisation Diffusion is spectral autoregression.https://sander

Reference 2007

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:20:20.008858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fd8c5e43-c480-461d-b226-0a1c45218565 · outbound

This paper cites Elucidating the Design Space of Diffusion-Based Generative Models.

A multi-scale loss formulation for learning a probabilistic model with proper score optimisation Elucidating the Design Space of Diffusion-Based Generative Models

Reference 2008

Resolution: unresolved; no resolver link, observed 2026-08-07T04:20:19.619065Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 916f7081-394f-4e60-bc3a-0dfa9e95ca90 · outbound

This paper cites doi: 10.1002/qj.2270.

A multi-scale loss formulation for learning a probabilistic model with proper score optimisation doi: 10.1002/qj.2270

Reference 2013

Resolution: unresolved; no resolver link, observed 2026-08-07T04:20:19.589196Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d9d6b3a-07be-4024-9b6f-18ab6d66e957 · outbound

This paper cites an unresolved cited work.

A multi-scale loss formulation for learning a probabilistic model with proper score optimisation Unresolved cited work

Reference 2014

Resolution: unresolved; no resolver link, observed 2026-08-07T04:20:19.780671Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b36c6349-29ca-432b-b838-fa7e1fb5f741 · outbound

This paper cites Deep Unsupervised Learning using Nonequilibrium Thermodynamics.

A multi-scale loss formulation for learning a probabilistic model with proper score optimisation Deep Unsupervised Learning using Nonequilibrium Thermodynamics

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-07T04:20:19.774177Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64e3c400-97b2-4da5-a578-6881f0498e7a · outbound

This paper cites Franco Molteni, Roberto Buizza, Tim N Palmer, and Thomas Petroliagis.

A multi-scale loss formulation for learning a probabilistic model with proper score optimisation Franco Molteni, Roberto Buizza, Tim N Palmer, and Thomas Petroliagis

Reference 2017

Resolution: verified exact; doi, observed 2026-08-07T04:20:19.819732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation de9b4dad-f8b9-4ae6-a610-9a1ac46e12d1 · outbound

This paper cites URLhttps://rmets.onlinelibrary.

A multi-scale loss formulation for learning a probabilistic model with proper score optimisation URLhttps://rmets.onlinelibrary

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-07T04:20:19.762683Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3160bfc7-2801-45f2-8fd8-32125a5b94b5 · outbound

This paper cites Hans Hersbach.

A multi-scale loss formulation for learning a probabilistic model with proper score optimisation Hans Hersbach

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-07T04:20:19.596951Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 54ce8089-4cea-414a-9c4d-b6cccd62bd45 · outbound

This paper cites Erik Larsson, Joel Oskarsson, Tomas Landelius, and Fredrik Lindsten.

A multi-scale loss formulation for learning a probabilistic model with proper score optimisation Erik Larsson, Joel Oskarsson, Tomas Landelius, and Fredrik Lindsten

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-07T04:20:19.756087Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 724b8055-8c90-4555-841e-b539acfd97f6 · outbound

This paper cites Neural General Circulation Models for Weather and Climate.

A multi-scale loss formulation for learning a probabilistic model with proper score optimisation Neural General Circulation Models for Weather and Climate

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-07T04:20:19.681515Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1293152e-80f6-48a9-b006-7f5984569f65 · outbound

This paper cites AIFS -- ECMWF's data-driven forecasting system.

A multi-scale loss formulation for learning a probabilistic model with proper score optimisation AIFS -- ECMWF's data-driven forecasting system

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-07T04:20:19.729830Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dcad0951-d96b-494e-8556-15e6a6065534 · outbound

This paper cites Blog post.

A multi-scale loss formulation for learning a probabilistic model with proper score optimisation Blog post

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:20:20.000117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5c52e7d5-ae1b-499a-ac81-1f1fe5b2f87a · outbound

This paper cites Diffusion-LAM: Probabilistic Limited Area Weather Forecasting with Diffusion.

A multi-scale loss formulation for learning a probabilistic model with proper score optimisation Diffusion-LAM: Probabilistic Limited Area Weather Forecasting with Diffusion

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-07T04:20:19.759298Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation f8c696c6-a114-481d-bf71-a454708269af · inbound

CRPS-LAM: Probabilistic Regional Weather Forecasting with Continuous Ranked Probability Score cites this paper.

CRPS-LAM: Probabilistic Regional Weather Forecasting with Continuous Ranked Probability Score A multi-scale loss formulation for learning a probabilistic model with proper score optimisation

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T10:37:41.543599Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 694bf7b0-ba86-4a0a-998a-5ce6f067afa1 · inbound

HourGlass: A probabilistic data-driven temporal downscaler for global and regional weather forecasting cites this paper.

HourGlass: A probabilistic data-driven temporal downscaler for global and regional weather forecasting A multi-scale loss formulation for learning a probabilistic model with proper score optimisation

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-14T05:28:17.350967Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.