Paper Citation Record · LEDGER

All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:1801.01489.

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

pith.paper-citation-record.v1
1801.01489 v5

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:07:40.723311Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-24T04:23:52.826832Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 3f2dd07f-a24c-4187-9768-059da46f0eb5 · inbound

Efficient computation of counterfactual explanations of LVQ models cites this paper.

Efficient computation of counterfactual explanations of LVQ models All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-14T15:39:39.294815Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 73ecad94-60de-4aca-af1c-186b55b4a364 · inbound

Shapley Decomposition of R-Squared in Machine Learning Models cites this paper.

Shapley Decomposition of R-Squared in Machine Learning Models All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-14T11:09:33.573947Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 242b2578-f061-4583-af33-c3eb37f03ca0 · inbound

Industry Practitioners Perspectives on AI Model Quality: Perceptions, Challenges, and Solutions cites this paper.

Industry Practitioners Perspectives on AI Model Quality: Perceptions, Challenges, and Solutions All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-24T04:23:52.829193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 247cdf17-f240-45ad-9cf9-9e9254133885 · inbound

AI-Spectra: A Visual Dashboard for Model Multiplicity to Enhance Informed and Transparent Decision-Making cites this paper.

AI-Spectra: A Visual Dashboard for Model Multiplicity to Enhance Informed and Transparent Decision-Making All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-12T20:24:47.041271Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29ff2fed-863f-4311-8baf-d6a27e77832c · inbound

How Your Location Relates to Health: Variable Importance and Interpretable Machine Learning for Environmental and Sociodemographic Data cites this paper.

How Your Location Relates to Health: Variable Importance and Interpretable Machine Learning for Environmental and Sociodemographic Data All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T22:19:44.575672Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 568ed3e5-b21f-45f3-8ed5-81a566c5ac5f · inbound

Automatic detection of Ellerman bombs using Deep Learning cites this paper.

Automatic detection of Ellerman bombs using Deep Learning All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-16T00:07:40.723311Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa290208-8468-4ca7-ba1d-56d833df842a · inbound

Explainable AI the Latest Advancements and New Trends cites this paper.

Explainable AI the Latest Advancements and New Trends All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-15T22:29:34.851961Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c19c4e32-a87c-488e-87e3-d9f7bd5f3a3c · inbound

Interpretable Event Diagnosis in Water Distribution Networks cites this paper.

Interpretable Event Diagnosis in Water Distribution Networks All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-15T22:27:53.662220Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22bda1e0-7008-4695-84ee-b8d1d9397072 · inbound

Towards Reliable Testing of Machine Unlearning cites this paper.

Towards Reliable Testing of Machine Unlearning All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-10T11:25:18.318896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3c68f210-8ba9-4c97-88e9-5913c5bbe371 · inbound

Scaling Inherently Interpretable Language Models cites this paper.

Scaling Inherently Interpretable Language Models All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously

Reference 188

Resolution: unresolved; no resolver link, observed 2026-08-11T00:36:59.145130Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 51b610e5-50c2-4439-837e-ccbcaacc2870 · inbound

On the global feature importance for interpretable and trustworthy heat demand forecasting cites this paper.

On the global feature importance for interpretable and trustworthy heat demand forecasting All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T18:07:05.187858Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.