Paper Citation Record · LEDGER

A Framework for Supervised and Unsupervised Segmentation and Classification of Materials Microstructure Images

As of 15 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:2502.07107.

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

pith.paper-citation-record.v1
2502.07107 v2

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T13:50:35.252891Z

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

12 of 12 outbound references displayed

verified exact0
verified fuzzy10
unresolved2
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation add058b9-cde3-456e-88c3-c88cf26758d7 · outbound

This paper cites AFRL (2018) Air Force Research Laboratory (AFRL) additive manufac- turing (AM) modeling challenge series.,.

A Framework for Supervised and Unsupervised Segmentation and Classification of Materials Microstructure Images AFRL (2018) Air Force Research Laboratory (AFRL) additive manufac- turing (AM) modeling challenge series.,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:50:35.470613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b1e33936-1aa6-44e9-8681-0c31419b2f57 · outbound

This paper cites Extraction of reduced-order process-structure linkages from phase-field simulations,.

A Framework for Supervised and Unsupervised Segmentation and Classification of Materials Microstructure Images Extraction of reduced-order process-structure linkages from phase-field simulations,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:50:35.342398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 01f02783-6483-440d-90d9-3ddf6f405c10 · outbound

This paper cites Unleashing the Power of Self-Supervised Image Denoising: A Comprehensive Review.

A Framework for Supervised and Unsupervised Segmentation and Classification of Materials Microstructure Images Unleashing the Power of Self-Supervised Image Denoising: A Comprehensive Review

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-08T13:50:35.252891Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da38f89f-024b-4621-9609-63fe3e645b7e · outbound

This paper cites Filtering for texture classification: A comparative study,.

A Framework for Supervised and Unsupervised Segmentation and Classification of Materials Microstructure Images Filtering for texture classification: A comparative study,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:50:35.407889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3506ec24-edfa-4672-ad83-be4d0cb005cb · outbound

This paper cites XSEDE: accelerating scientific discovery,.

A Framework for Supervised and Unsupervised Segmentation and Classification of Materials Microstructure Images XSEDE: accelerating scientific discovery,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:50:35.358527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5f18069c-c256-41f5-a0a6-b936267af953 · outbound

This paper cites Optimizing convo- lutional neural networks to perform semantic segmentation on large materials imaging datasets: X-ray tomography and serial sectioning,.

A Framework for Supervised and Unsupervised Segmentation and Classification of Materials Microstructure Images Optimizing convo- lutional neural networks to perform semantic segmentation on large materials imaging datasets: X-ray tomography and serial sectioning,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:50:35.374862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 309df713-e88b-4013-92de-d749fcffe397 · outbound

This paper cites Incremental learning to segment micrographs,.

A Framework for Supervised and Unsupervised Segmentation and Classification of Materials Microstructure Images Incremental learning to segment micrographs,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:50:35.439580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 39408499-a74e-42d0-a59a-dcdc0002af17 · outbound

This paper cites Characterization and design of functional quasi- random nanostructured materials using spectral density function,.

A Framework for Supervised and Unsupervised Segmentation and Classification of Materials Microstructure Images Characterization and design of functional quasi- random nanostructured materials using spectral density function,

Reference 258

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:50:35.326281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 64ffe5f6-9c01-4880-a719-298606af9a95 · outbound

This paper cites Imagenet large scale visual recognition challenge,.

A Framework for Supervised and Unsupervised Segmentation and Classification of Materials Microstructure Images Imagenet large scale visual recognition challenge,

Reference 370

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:50:35.390690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c64e544b-4d89-42cd-8d79-fe4d9cae5243 · outbound

This paper cites Additive manufacturing of ceramic compo- nents,.

A Framework for Supervised and Unsupervised Segmentation and Classification of Materials Microstructure Images Additive manufacturing of ceramic compo- nents,

Reference 617

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:50:35.424489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b708cec6-3175-4fb5-8069-f34d7c9a8573 · outbound

This paper cites Fundamentals, processes and applications of high-permittivity polymer–matrix composites,.

A Framework for Supervised and Unsupervised Segmentation and Classification of Materials Microstructure Images Fundamentals, processes and applications of high-permittivity polymer–matrix composites,

Reference 1258

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:50:35.455262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ea6af1c2-143e-4dee-bf83-1a0a3e845fd8 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

A Framework for Supervised and Unsupervised Segmentation and Classification of Materials Microstructure Images Adam: A Method for Stochastic Optimization

Reference 3900

Resolution: unresolved; no resolver link, observed 2026-08-08T13:50:35.211072Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.