Paper Citation Record · LEDGER

Emulating the Global Change Analysis Model with Deep Learning

As of 21 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 1 inbound Pith citation observation for arXiv:2412.08850.

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

pith.paper-citation-record.v1
2412.08850 v1

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T17:32:49.493769Z

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-26T18:43:06.382026Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

19 of 19 outbound references displayed

verified exact0
verified fuzzy14
unresolved5
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

1
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 27cf3661-5207-4776-ad85-ef93fde11185 · outbound

This paper cites Experiment tracking with weights and biases, 2020.

Emulating the Global Change Analysis Model with Deep Learning Experiment tracking with weights and biases, 2020

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-11T17:32:49.410854Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1f9e0c4d-1c45-42e8-bf4a-4672cdb9e51f · outbound

This paper cites an unresolved cited work.

Emulating the Global Change Analysis Model with Deep Learning Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-11T17:32:49.784306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 08c7cbfa-f3e0-4d87-8561-72b26370d1ad · outbound

This paper cites Smith, Abigail Snyder, Stephanie Waldhoff, and Marshall Wise.

Emulating the Global Change Analysis Model with Deep Learning Smith, Abigail Snyder, Stephanie Waldhoff, and Marshall Wise

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:49.769243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9be67887-d8e9-406e-9e91-47fd9292f46b · outbound

This paper cites Narayan, Alan V.

Emulating the Global Change Analysis Model with Deep Learning Narayan, Alan V

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:49.754066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6ac29c45-1066-4d66-92d4-cad015d9749b · outbound

This paper cites Evaluating the economic impact of water scarcity in a changing world.

Emulating the Global Change Analysis Model with Deep Learning Evaluating the economic impact of water scarcity in a changing world

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:49.738916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c62fe954-605b-4e76-b3a4-48fcd099b538 · outbound

This paper cites Visual Feature Extraction by a Multilayered Network of Analog Threshold Elements.

Emulating the Global Change Analysis Model with Deep Learning Visual Feature Extraction by a Multilayered Network of Analog Threshold Elements

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:49.723524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e7729ee4-fc04-440a-b42f-00138b07d720 · outbound

This paper cites Deep Learning.

Emulating the Global Change Analysis Model with Deep Learning Deep Learning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T17:32:49.442456Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28093192-ece1-4a92-8762-0dfe6762d501 · outbound

This paper cites SALib: An open-source python library for sensitivity analysis.

Emulating the Global Change Analysis Model with Deep Learning SALib: An open-source python library for sensitivity analysis

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:49.696866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3de4a14b-2fa8-49e1-bfbc-23d5ec5d7391 · outbound

This paper cites Toward SALib 2.0: Advancing the accessibility and interpretability of global sensitivity analyses.

Emulating the Global Change Analysis Model with Deep Learning Toward SALib 2.0: Advancing the accessibility and interpretability of global sensitivity analyses

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:49.682022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 84b44496-a051-4655-803a-c0ced7115464 · outbound

This paper cites Compounding uncertainties in economic and population growth increase tail risks for relevant outcomes across sectors.

Emulating the Global Change Analysis Model with Deep Learning Compounding uncertainties in economic and population growth increase tail risks for relevant outcomes across sectors

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:49.666917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation aafa7d2f-0abd-48f1-b76a-257a905699db · outbound

This paper cites Decoupled Weight Decay Regularization.

Emulating the Global Change Analysis Model with Deep Learning Decoupled Weight Decay Regularization

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:49.652697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7d80fe81-56f3-462e-9feb-25074dda7b1b · outbound

This paper cites an unresolved cited work.

Emulating the Global Change Analysis Model with Deep Learning Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-08-11T17:32:49.638051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation cb7f0aaa-beb1-47b4-b5d0-51d165772db0 · outbound

This paper cites Global Sensitivity Analysis: The Primer.

Emulating the Global Change Analysis Model with Deep Learning Global Sensitivity Analysis: The Primer

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:49.624615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation cb6de8a0-31a7-4a7a-bc8d-8a7eb9179d10 · outbound

This paper cites Practical bayesian optimization of machine learning algorithms.

Emulating the Global Change Analysis Model with Deep Learning Practical bayesian optimization of machine learning algorithms

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:49.609827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8c842675-cb17-413b-a1fc-c1dc4c5397a3 · outbound

This paper cites Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates.

Emulating the Global Change Analysis Model with Deep Learning Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:49.592501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 17cbca3f-736d-4a5c-a122-b9225ee0fe92 · outbound

This paper cites Sobol’ and S.

Emulating the Global Change Analysis Model with Deep Learning Sobol’ and S

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:49.577412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8bf0390c-0d9c-4eb0-a077-08b8759cd7d9 · outbound

This paper cites Reproducing complex simulations of economic impacts of climate change with lower-cost emulators.

Emulating the Global Change Analysis Model with Deep Learning Reproducing complex simulations of economic impacts of climate change with lower-cost emulators

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:49.561835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation fa5d0dd1-57ca-4314-91ba-185c2361c914 · outbound

This paper cites Woodard, Abigail Snyder, Jonathan R.

Emulating the Global Change Analysis Model with Deep Learning Woodard, Abigail Snyder, Jonathan R

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:49.545641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b0886b91-daf7-4511-a063-8af9d91f30d0 · outbound

This paper cites an unresolved cited work.

Emulating the Global Change Analysis Model with Deep Learning Unresolved cited work

Reference 19

Resolution: unresolved; raw_fallback, observed 2026-08-11T17:32:49.529807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

Observation 14e932d6-cdd9-4431-a2c4-0f7db45c948f · inbound

Optimal scenario design for climate emulation cites this paper.

Optimal scenario design for climate emulation Emulating the Global Change Analysis Model with Deep Learning

Reference 291

Resolution: verified exact; arxiv_id, observed 2026-06-26T18:49:44.185476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.