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Paper Citation Record · LEDGER

Cross-Process Defect Attribution using Potential Loss Analysis

As of 21 August 2026, this Paper Citation Record lists 9 of 9 outbound references and 0 inbound Pith citation observations for arXiv:2508.00895.

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

pith.paper-citation-record.v1
2508.00895 v1

Coverage vector

measured 9 of 9 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T17:52:30.612043Z

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

9 of 9 outbound references displayed

  • verified exact1
  • verified fuzzy7
  • unresolved1
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1e5be640-b757-41e1-8156-82259e3ba9f6 · outbound

This paper cites Improved Yield Prediction and Failure Analysis in Semiconductor Man- ufacturing with XGBoost and Shapley Additive exPlanations Models.

Cross-Process Defect Attribution using Potential Loss Analysis Improved Yield Prediction and Failure Analysis in Semiconductor Man- ufacturing with XGBoost and Shapley Additive exPlanations Models

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T17:52:30.727040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T17:52:30.605081Z digest=sha256:1f2477ac4f1c6cac6f2f1c15e32ef179069b0ec5e37efecefc23602d3a64f0bd

Observation 564ec91f-c441-47b1-8be4-4e2a761ff5cd · outbound

This paper cites Soft-sensing conformer: A curriculum learning-based convolutional transformer.

Cross-Process Defect Attribution using Potential Loss Analysis Soft-sensing conformer: A curriculum learning-based convolutional transformer

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T17:52:30.706304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T17:52:30.612043Z digest=sha256:d605bed9ad2efd8b8f38a68daeccd96986fadabecadd214d65d14f27e571b153

Observation f84daf31-3385-400e-98dd-6dbb202960b8 · outbound

This paper cites Travel-time prediction using Gaussian process regression: A trajectory-based approach.

Cross-Process Defect Attribution using Potential Loss Analysis Travel-time prediction using Gaussian process regression: A trajectory-based approach

Reference 2009

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T17:52:30.780859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T17:52:30.574052Z digest=sha256:fee77d3d52de8e364755f10780a1e6affe12d3f61e8f8de4146362ddd0ec7420

Observation e93a1276-f5b3-4718-aab3-728dc891a25b · outbound

This paper cites Machine Learning Assisted New Product Setup.

Cross-Process Defect Attribution using Potential Loss Analysis Machine Learning Assisted New Product Setup

Reference 2020

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T17:52:30.744955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T17:52:30.599453Z digest=sha256:a25cd88fe64aee3bbb31d1995133906ae195a2cf38619bfcc29397b87d6439cf

Observation aea48f78-aced-4b4e-b753-5365dce539f9 · outbound

This paper cites Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey.

Cross-Process Defect Attribution using Potential Loss Analysis Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-15T17:52:30.586506Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T17:52:30.586506Z digest=sha256:6d74a43268ce73cc20cc6d30ef673637371238042467a68e5e5255cf69504aa6

Observation 86529e4c-bde2-4591-9e4a-4ccb3e1df6b9 · outbound

This paper cites Graph representation and embedding for semiconductor manufacturing fab states.

Cross-Process Defect Attribution using Potential Loss Analysis Graph representation and embedding for semiconductor manufacturing fab states

Reference 2022

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T17:52:30.763092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T17:52:30.593681Z digest=sha256:deb568924a9e9a22ad50b324b61a1232203dd19fe1149c248706a62ffa5c4865

Observation 64e91630-4322-4bb3-903b-03abdc60a801 · outbound

This paper cites Deep learning-based virtual metrology in multivariate time series.

Cross-Process Defect Attribution using Potential Loss Analysis Deep learning-based virtual metrology in multivariate time series

Reference 2023

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T17:52:30.800213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T17:52:30.568770Z digest=sha256:c36eea440d70bc3c8bb42e063fffb87c5438307084b760d46bc5070939bc1e70

Observation 5df7cc77-ae5e-42d5-b4d1-beba683039f3 · outbound

This paper cites Enhanced Yield Prediction in Semiconductor Manufacturing: Innovative Strategies for Imbalanced Sample Management and Root Cause Analysis.

Cross-Process Defect Attribution using Potential Loss Analysis Enhanced Yield Prediction in Semiconductor Manufacturing: Innovative Strategies for Imbalanced Sample Management and Root Cause Analysis

Reference 2024

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T17:52:30.818261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T17:52:30.562445Z digest=sha256:5d5d5b28550628f9ff8d36139a7071d95099e13c6f24ec457005fecd0a9da8fb

Observation 1ff0286d-deb8-4928-82f8-7b68c8bea277 · outbound

This paper cites Path Learning with Trajectory Advantage Regression.

Cross-Process Defect Attribution using Potential Loss Analysis Path Learning with Trajectory Advantage Regression

Reference 2025

Resolution
verified exact
local_arxiv, observed 2026-08-15T17:52:30.662070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T17:52:30.579434Z digest=sha256:fb7dc662cb2cb51a227b19a1ce41f32914f6405a0ff23276b1a618e0cbb44351

Pith citing papers

No inbound Pith citation observations are available.