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Paper Citation Record · LEDGER

Beyond Synthetic Augmentation: Group-Aware Threshold Calibration for Robust Balanced Accuracy in Imbalanced Learning

As of 10 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2509.02592.

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

pith.paper-citation-record.v1
2509.02592 v1

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T14:23:20.216555Z

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

16 of 16 outbound references displayed

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  • verified fuzzy0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bbd4e0cf-2f21-404b-9e70-f293fe3b6ee8 · outbound

This paper cites BMC Bioinformatics 14(1), 106 (Dec 2013).

Beyond Synthetic Augmentation: Group-Aware Threshold Calibration for Robust Balanced Accuracy in Imbalanced Learning BMC Bioinformatics 14(1), 106 (Dec 2013)

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 78552f40-8e8a-44a5-8634-733d3e289383 · outbound

This paper cites SMOTE: Synthetic Minority Over-sampling Technique.

Beyond Synthetic Augmentation: Group-Aware Threshold Calibration for Robust Balanced Accuracy in Imbalanced Learning SMOTE: Synthetic Minority Over-sampling Technique

Reference 2

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Observation 5643878f-2c4a-4702-84ab-eff977c02aca · outbound

This paper cites Algorithmic decision making and the cost of fairness.

Beyond Synthetic Augmentation: Group-Aware Threshold Calibration for Robust Balanced Accuracy in Imbalanced Learning Algorithmic decision making and the cost of fairness

Reference 3

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no resolver link, observed 2026-08-05T14:23:18.757038Z

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Observation 49ddde33-5be9-4b96-80a3-67bbf208f1de · outbound

This paper cites OxonFair: A Flexible Toolkit for Algorithmic Fairness.

Beyond Synthetic Augmentation: Group-Aware Threshold Calibration for Robust Balanced Accuracy in Imbalanced Learning OxonFair: A Flexible Toolkit for Algorithmic Fairness

Reference 4

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Observation ca96d674-ab87-43a7-bc84-6c99388d4f7d · outbound

This paper cites Conditional Wasserstein GAN-based Oversampling of Tabular Data for Imbalanced Learning.

Beyond Synthetic Augmentation: Group-Aware Threshold Calibration for Robust Balanced Accuracy in Imbalanced Learning Conditional Wasserstein GAN-based Oversampling of Tabular Data for Imbalanced Learning

Reference 5

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Observation 9d53d3d2-713c-4c7a-a2c1-106ef298c4e9 · outbound

This paper cites Journal of Chemical Information and Modeling61(6), 2623–2640 (Jun 2021).

Beyond Synthetic Augmentation: Group-Aware Threshold Calibration for Robust Balanced Accuracy in Imbalanced Learning Journal of Chemical Information and Modeling61(6), 2623–2640 (Jun 2021)

Reference 6

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no resolver link, observed 2026-08-05T14:23:19.011009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 43a9bd4e-c385-4c01-9973-5c324dba7f5f · outbound

This paper cites IEEE Transactions on Knowledge and Data Engineering21(9), 1263–1284 (Sep 2009).

Beyond Synthetic Augmentation: Group-Aware Threshold Calibration for Robust Balanced Accuracy in Imbalanced Learning IEEE Transactions on Knowledge and Data Engineering21(9), 1263–1284 (Sep 2009)

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation ff52c57d-e757-435a-9d1b-cd7cc3a7fbf7 · outbound

This paper cites Equality of Opportunity in Supervised Learning.

Beyond Synthetic Augmentation: Group-Aware Threshold Calibration for Robust Balanced Accuracy in Imbalanced Learning Equality of Opportunity in Supervised Learning

Reference 8

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Observation d01bd10d-8b70-4af0-b458-32eba27386b3 · outbound

This paper cites Stop Oversampling for Class Imbalance Learning: A Critical Review.

Beyond Synthetic Augmentation: Group-Aware Threshold Calibration for Robust Balanced Accuracy in Imbalanced Learning Stop Oversampling for Class Imbalance Learning: A Critical Review

Reference 9

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Observation 7dc22a35-a775-4aec-8027-ef83d6eb690c · outbound

This paper cites Applied Soft Computing83, 105662 (Oct 2019).

Beyond Synthetic Augmentation: Group-Aware Threshold Calibration for Robust Balanced Accuracy in Imbalanced Learning Applied Soft Computing83, 105662 (Oct 2019)

Reference 10

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metadata mismatch
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 13a2914e-61b0-4077-9b86-de6ce891f100 · outbound

This paper cites Just Train Twice: Improving Group Robustness without Training Group Information.

Beyond Synthetic Augmentation: Group-Aware Threshold Calibration for Robust Balanced Accuracy in Imbalanced Learning Just Train Twice: Improving Group Robustness without Training Group Information

Reference 11

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Observation 14469329-22cd-472e-a802-5a52e1d61425 · outbound

This paper cites Pattern Recognition 91, 216–231 (Jul 2019).

Beyond Synthetic Augmentation: Group-Aware Threshold Calibration for Robust Balanced Accuracy in Imbalanced Learning Pattern Recognition 91, 216–231 (Jul 2019)

Reference 12

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Observation b45a7bea-abba-4063-b7e6-3733fa5473da · outbound

This paper cites Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization.

Beyond Synthetic Augmentation: Group-Aware Threshold Calibration for Robust Balanced Accuracy in Imbalanced Learning Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization

Reference 13

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 0cc4d388-f986-4106-8344-4c74c7a90415 · outbound

This paper cites IEEE Computational Intelligence Magazine 13(4), 59–76 (Nov 2018).

Beyond Synthetic Augmentation: Group-Aware Threshold Calibration for Robust Balanced Accuracy in Imbalanced Learning IEEE Computational Intelligence Magazine 13(4), 59–76 (Nov 2018)

Reference 14

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation a26fe75d-cda4-4e0d-bcf2-4cc0be4bf78d · outbound

This paper cites In: 2011 IEEE 11th International Conference on Data Mining.

Beyond Synthetic Augmentation: Group-Aware Threshold Calibration for Robust Balanced Accuracy in Imbalanced Learning In: 2011 IEEE 11th International Conference on Data Mining

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation a2c534f4-6fd9-487b-aadd-577881027b0c · outbound

This paper cites Modeling Tabular data using Conditional GAN.

Beyond Synthetic Augmentation: Group-Aware Threshold Calibration for Robust Balanced Accuracy in Imbalanced Learning Modeling Tabular data using Conditional GAN

Reference 16

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Pith citing papers

No inbound Pith citation observations are available.