Paper Citation Record · LEDGER

SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers

As of 20 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2505.16330.

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

pith.paper-citation-record.v1
2505.16330 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:05:28.230038Z

measured 45 of 45 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

45 of 45 outbound references displayed

verified exact8
verified fuzzy5
unresolved23
parse uncertain0
malformed identifier2
metadata mismatch7

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation af8d40d3-da35-4aa1-a454-4e2114e87a8e · outbound

This paper cites doi:https://dl.

SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers doi:https://dl

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T15:05:24.700248Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 361107e5-8318-437d-87ea-c5f53d82a6a3 · outbound

This paper cites The Electronic Library doi:https://doi.org/10.1108/EL-03-2024-0070.

SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers The Electronic Library doi:https://doi.org/10.1108/EL-03-2024-0070

Reference 7

Resolution: verified exact; doi, observed 2026-08-07T15:05:29.782417Z

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.

Observation 6c1f130c-2354-4639-9d40-5fdd87cafcfa · outbound

This paper cites Expert Systems with Applications , 125533doi:https: //doi.org/10.1016/j.eswa.2024.125533.

SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers Expert Systems with Applications , 125533doi:https: //doi.org/10.1016/j.eswa.2024.125533

Reference 9

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T15:05:31.619779Z

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.

Observation b6fc9eb0-6c2e-4d15-80c1-77e3379b93a5 · outbound

This paper cites Reviewer2: Optimizing Review Generation Through Prompt Generation.

SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers Reviewer2: Optimizing Review Generation Through Prompt Generation

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T15:05:25.501999Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b7d4ac05-43b0-4440-83a5-cbdb310a21b0 · outbound

This paper cites Journal of Informetrics 16, 101306.

SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers Journal of Informetrics 16, 101306

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T15:05:25.828313Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a333f37a-67f8-4073-9289-9755ef8b3e82 · outbound

This paper cites Journal of Infor- metrics 17, 101450.

SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers Journal of Infor- metrics 17, 101450

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T15:05:25.943595Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8010e1d2-7f41-48ed-9955-cd68c3319efc · outbound

This paper cites an unresolved cited work.

SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers Unresolved cited work

Reference 17

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T15:05:31.043833Z

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.

Observation 682d295b-05fa-44a6-8e2c-e2ec1f79b6a5 · outbound

This paper cites (Eds.), Proceedings of the 40 24th Annual Conference of the European Association for Machine Trans- lation, European Association for Machine Translation, Tampere, Finland.

SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers (Eds.), Proceedings of the 40 24th Annual Conference of the European Association for Machine Trans- lation, European Association for Machine Translation, Tampere, Finland

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:05:33.267595Z

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.

Observation 90719538-d435-4176-a4ef-0b07b74a928f · outbound

This paper cites NEJM AI 0, AIoa2400196.

SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers NEJM AI 0, AIoa2400196

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T15:05:26.124847Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d5617dc0-1d98-4b91-83c9-b83fb54db2e5 · outbound

This paper cites Information Processing & Manage- ment 61, 103743.

SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers Information Processing & Manage- ment 61, 103743

Reference 20

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T15:05:30.775710Z

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.

Observation 38603b02-d79c-4685-9479-5a41f3a8f66a · outbound

This paper cites ReviewerGPT? An Exploratory Study on Using Large Language Models for Paper Reviewing.

SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers ReviewerGPT? An Exploratory Study on Using Large Language Models for Paper Reviewing

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T15:05:26.293474Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c9214b50-6ded-421e-b3fe-94274ff7cc6a · outbound

This paper cites (Eds.), Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Association for Com- putational Linguistics, Online.

SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers (Eds.), Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Association for Com- putational Linguistics, Online

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T15:05:26.398727Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6398b8bb-2dd9-4728-aa27-fa5c9ba40f3c · outbound

This paper cites Journal of Infor- metrics 16, 101282.

SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers Journal of Infor- metrics 16, 101282

Reference 24

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T15:05:30.612528Z

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.

Observation 22d4fdf7-3b94-46cf-8df8-17fad58bbd99 · outbound

This paper cites Scientometrics 126, 6891–6915.

SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers Scientometrics 126, 6891–6915

Reference 25

Resolution: verified exact; doi, observed 2026-08-07T15:05:29.219322Z

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.

Observation 4919c1f0-feee-4ed2-ab64-aaf6afd5b9ef · outbound

This paper cites GPT-4 Technical Report.

SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers GPT-4 Technical Report

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T15:05:26.806605Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f252d737-bc67-488e-a723-0b73b6772793 · outbound

This paper cites (Eds.), Advances in Neural Information Processing Systems, Curran Associates, Inc.

SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers (Eds.), Advances in Neural Information Processing Systems, Curran Associates, Inc

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:05:33.099318Z

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.

Observation d577f2c0-e563-4d7e-a4e7-721d4230489a · outbound

This paper cites Expert Systems with Applications 256, 124912.

SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers Expert Systems with Applications 256, 124912

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T15:05:26.976636Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ac05504-0321-4330-98a3-5458eced5374 · outbound

This paper cites Aslib Journal of Information Management 75, 884–916.

SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers Aslib Journal of Information Management 75, 884–916

Reference 30

Resolution: verified exact; doi, observed 2026-08-07T15:05:28.876975Z

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.

Observation 40f1571d-8121-4d95-8fff-753dede73594 · outbound

This paper cites GPT4 is Slightly Helpful for Peer-Review Assistance: A Pilot Study.

SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers GPT4 is Slightly Helpful for Peer-Review Assistance: A Pilot Study

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T15:05:27.180000Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9f0650e6-75bf-41db-ae35-e1a81be9c477 · outbound

This paper cites Business Cycles: A Theoretical, Historical, and Statistical Analysis of the Capitalist Process, Martino Pub.

SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers Business Cycles: A Theoretical, Historical, and Statistical Analysis of the Capitalist Process, Martino Pub

Reference 33

Resolution: verified exact; doi, observed 2026-08-07T15:05:28.660616Z

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.

Observation b3e0fa31-acdb-44f1-83e5-1235f6d4b21e · outbound

This paper cites Expert Systems with Applications , 125509doi:https://doi.org/10.1016/j.eswa.2024.125509.

SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers Expert Systems with Applications , 125509doi:https://doi.org/10.1016/j.eswa.2024.125509

Reference 34

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T15:05:30.361994Z

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.

Observation ef9e8f19-5b1c-41e8-bfd6-110ba66ceb76 · outbound

This paper cites (Eds.), Findings of the Association for Computational Linguistics: EMNLP 2023, Association for Compu- tational Linguistics, Singapore.

SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers (Eds.), Findings of the Association for Computational Linguistics: EMNLP 2023, Association for Compu- tational Linguistics, Singapore

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T15:05:27.580103Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d959d6f1-57a3-433a-ab91-f83017cd3bfb · outbound

This paper cites doi:https://doi.org/ 10.1016/j.joi.2018.07.005.

SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers doi:https://doi.org/ 10.1016/j.joi.2018.07.005

Reference 36

Resolution: verified exact; doi, observed 2026-08-07T15:05:28.460480Z

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.

Observation 574c2e32-defa-43ef-b004-5095d9c386dd · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers LLaMA: Open and Efficient Foundation Language Models

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T15:05:27.703912Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cdd1d452-e774-411f-b167-54fa66cdd36e · outbound

This paper cites Research Policy 46, 1416–1436.

SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers Research Policy 46, 1416–1436

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T15:05:27.850736Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd741d5c-93cd-4f03-b664-863172ce02c1 · outbound

This paper cites Journal of Informetrics 18, 101587.

SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers Journal of Informetrics 18, 101587

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T15:05:27.904255Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d3db4e77-94f6-4725-89b5-b6735e581fea · outbound

This paper cites Plos one 18, e0284567.

SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers Plos one 18, e0284567

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T15:05:27.966163Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7ae61984-241b-4984-ae11-e50bc29f83e5 · outbound

This paper cites (Eds.), Advances in Neu- ral Information Processing Systems, Curran Associates, Inc.

SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers (Eds.), Advances in Neu- ral Information Processing Systems, Curran Associates, Inc

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:05:32.892263Z

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.

Observation e6c6e951-84cc-487f-9876-2c22a32a6abb · outbound

This paper cites (Eds.), Proceedings of the 2024 Joint International Conference on Computa- tional Linguistics, Language Resources and Evaluation (LREC-COLING 2024), ELRA and ICCL, Torino, Italia.

SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers (Eds.), Proceedings of the 2024 Joint International Conference on Computa- tional Linguistics, Language Resources and Evaluation (LREC-COLING 2024), ELRA and ICCL, Torino, Italia

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:05:32.647348Z

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.

Observation 9380a3d8-cced-451f-96ab-ed7883abdc1e · outbound

This paper cites deep learning for pa- per section identification: Toward applications in chinese medical liter- ature.

SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers deep learning for pa- per section identification: Toward applications in chinese medical liter- ature

Reference 44

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T15:05:30.108998Z

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.

Observation efc526b3-f676-4f1a-b2ec-ef07a5910fbd · outbound

This paper cites harvard university press.

SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers harvard university press

Reference 1985

Resolution: verified exact; doi, observed 2026-08-07T15:05:29.045720Z

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.

Observation b7039a8c-5ce0-4010-850b-e92a33c44976 · outbound

This paper cites doi:10.1177/000312240406900203.

SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers doi:10.1177/000312240406900203

Reference 2004

Resolution: verified exact; doi, observed 2026-08-07T15:05:29.598206Z

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.

Observation 653d8827-d814-467c-8348-51ba10eb4005 · outbound

This paper cites Oxford University Press.

SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers Oxford University Press

Reference 2006

Resolution: malformed identifier; no resolver link, observed 2026-08-07T15:05:25.283287Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 59b43588-f6f3-4c96-ae27-71c5a735f051 · outbound

This paper cites Cre- ativity Research Journal 24, 92–96.

SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers Cre- ativity Research Journal 24, 92–96

Reference 2012

Resolution: unresolved; no resolver link, observed 2026-08-07T15:05:27.257853Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c5849b11-2804-47a4-83ba-72524acb4a62 · outbound

This paper cites Sci- ence 342, 468–472.

SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers Sci- ence 342, 468–472

Reference 2013

Resolution: unresolved; no resolver link, observed 2026-08-07T15:05:27.781659Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1fe5eb60-467d-46a4-bac3-a6ef36705384 · outbound

This paper cites an unresolved cited work.

SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers Unresolved cited work

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-07T15:05:24.517970Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd548762-140a-42ab-9d1b-8d74ac450d28 · outbound

This paper cites an unresolved cited work.

SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers Unresolved cited work

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-07T15:05:25.390227Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 26281031-b959-46d9-97eb-ac166e996609 · outbound

This paper cites Scientometrics 115, 463–486.

SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers Scientometrics 115, 463–486

Reference 2018

Resolution: verified exact; doi, observed 2026-08-07T15:05:29.361796Z

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.

Observation 6e075715-d9c4-4d64-adff-4ca221a49889 · outbound

This paper cites an unresolved cited work.

SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers Unresolved cited work

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-07T15:05:24.278034Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f58750d4-443e-45a8-a186-cee16b3398ba · outbound

This paper cites Longformer: The Long-Document Transformer.

SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers Longformer: The Long-Document Transformer

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-07T15:05:24.408113Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1f5085bd-7473-4c16-90aa-5318db7724c9 · outbound

This paper cites Re- search Policy 50, 104144.

SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers Re- search Policy 50, 104144

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-07T15:05:24.195298Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ab2282b-ae64-4b6e-a0bf-2f7129a9bc16 · outbound

This paper cites (Eds.), Findings of the Association for Computational Linguistics: NAACL 2022, Association for Computational Linguistics, Seattle, United States.

SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers (Eds.), Findings of the Association for Computational Linguistics: NAACL 2022, Association for Computational Linguistics, Seattle, United States

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:05:33.410734Z

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.

Observation 89cd69f9-e125-4dbc-bad8-c590e628be02 · outbound

This paper cites Journal of Machine Learning Research 24, 1–113.

SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers Journal of Machine Learning Research 24, 1–113

Reference 2023

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T15:05:31.912321Z

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.

Observation 87621dd7-aa2f-4ae1-bcae-adbce27f41fd · outbound

This paper cites Expert Systems with Applications 235, 121186.

SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers Expert Systems with Applications 235, 121186

Reference 2024

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T15:05:32.169210Z

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.

Observation fb6ce203-8a72-4f07-840a-91266f0e935c · outbound

This paper cites Expert Systems with Applications 259, 125295.

SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers Expert Systems with Applications 259, 125295

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-07T15:05:28.230038Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.