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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 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 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 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T20:24:47.041271Z

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 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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-24T04:21:49.775278Z digest=sha256:202593847c2e838f905ff145cd929402ea3237acf5e9752c37ee8f8dd7bacaff

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.

source=pdf_text observed=2026-08-12T20:24:47.041271Z digest=sha256:2cb8942c3ee18fd3dd990b7f75ab17067896b4283c74c3dba5bb8f7f5f337d1e

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.

source=arxiv_source observed=2026-08-10T22:19:44.575672Z digest=sha256:64bc41c73562b942b583adac61119b5f6c39fc684d24f8a5b4a41ee7917f0e2e

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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-10T11:24:29.533446Z digest=sha256:27861717659ac4fd43660aa9721d641b225caf13f901913bf6cec177030bddc1

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.

source=arxiv_source observed=2026-08-11T00:36:59.145130Z digest=sha256:8b35d35dc1016f6b30ef7679786041f6f2a8b08da779a04e34b3b2094db3071c