Pith. sign in

Paper Citation Record · LEDGER

Lessons from the trenches on evaluating machine-learning systems in materials science

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2503.10837.

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

pith.paper-citation-record.v1
2503.10837 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:32:02.082275Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T12:06:02.131669Z

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 e7125f59-67a9-483d-98db-bcfafc7c6619 · inbound

The carbon cost of materials discovery: Can machine learning really accelerate the discovery of new photovoltaics? cites this paper.

The carbon cost of materials discovery: Can machine learning really accelerate the discovery of new photovoltaics? Lessons from the trenches on evaluating machine-learning systems in materials science

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-06T16:32:02.082275Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:32:02.082275Z digest=sha256:b8c4401e1caed6669c6cc0747d71d80e2ad9ccef9a2f1d5c7c72e3441f96477e

Observation 35a1bc22-238c-4c8b-aa5d-a9acae6c15d1 · inbound

Materials Informatics Across the Length Scales cites this paper.

Materials Informatics Across the Length Scales Lessons from the trenches on evaluating machine-learning systems in materials science

Reference 157

Resolution
verified exact
arxiv_id, observed 2026-05-11T12:06:02.138652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-10T04:13:12.315655Z digest=sha256:2f360be38a88bb8e5a050a15620313eb81d3bc6514a35b559caf198adb513de7