Paper Citation Record · LEDGER

Predicting Formula 1 Race Outcomes: Decomposing the Roles of Drivers and Constructors through Linear Modeling

As of 19 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2508.00200.

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

pith.paper-citation-record.v1
2508.00200 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T10:20:10.697336Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

25 of 25 outbound references displayed

verified exact1
verified fuzzy19
unresolved5
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 111de2ff-258b-4465-a354-3418fb03d337 · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

Predicting Formula 1 Race Outcomes: Decomposing the Roles of Drivers and Constructors through Linear Modeling On the Opportunities and Risks of Foundation Models

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f64a53b-2822-45f3-92ed-f6410548a9e5 · outbound

This paper cites Co-teaching: Robust training of deep neural networks with extremely noisy labels,.

Predicting Formula 1 Race Outcomes: Decomposing the Roles of Drivers and Constructors through Linear Modeling Co-teaching: Robust training of deep neural networks with extremely noisy labels,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:11.322613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation bee7a40c-b487-4e96-af47-e4334158574b · outbound

This paper cites Mentornet: Learning data-driven curriculum for deep neural networks on noisy labels,.

Predicting Formula 1 Race Outcomes: Decomposing the Roles of Drivers and Constructors through Linear Modeling Mentornet: Learning data-driven curriculum for deep neural networks on noisy labels,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:11.301500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f6a15fc6-030f-4373-afd8-bc1a09466af7 · outbound

This paper cites Selfie: Refurbishing unclean samples for robust deep learning,.

Predicting Formula 1 Race Outcomes: Decomposing the Roles of Drivers and Constructors through Linear Modeling Selfie: Refurbishing unclean samples for robust deep learning,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:11.277015Z

Source-reported events for the cited work

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Observation 3df40b3c-b916-4d76-953c-317dc1f236fd · outbound

This paper cites Dividemix: Learning with noisy labels as semi-supervised learning,.

Predicting Formula 1 Race Outcomes: Decomposing the Roles of Drivers and Constructors through Linear Modeling Dividemix: Learning with noisy labels as semi-supervised learning,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:11.247263Z

Source-reported events for the cited work

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Observation c7835f0b-a923-4056-a077-3988982588e8 · outbound

This paper cites Combating noisy labels by agreement: A joint training method with co-regularization,.

Predicting Formula 1 Race Outcomes: Decomposing the Roles of Drivers and Constructors through Linear Modeling Combating noisy labels by agreement: A joint training method with co-regularization,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:11.228097Z

Source-reported events for the cited work

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Observation 85bc6b5e-5767-485c-a4b5-4186da3c1eca · outbound

This paper cites ProMix: Combating Label Noise via Maximizing Clean Sample Utility.

Predicting Formula 1 Race Outcomes: Decomposing the Roles of Drivers and Constructors through Linear Modeling ProMix: Combating Label Noise via Maximizing Clean Sample Utility

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T10:20:10.593164Z

Source-reported events for the cited work

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Observation f58935d9-11b9-448e-87e4-e7905cdfefd3 · outbound

This paper cites Badlabel: A robust perspective on evaluating and enhancing label-noise learning,.

Predicting Formula 1 Race Outcomes: Decomposing the Roles of Drivers and Constructors through Linear Modeling Badlabel: A robust perspective on evaluating and enhancing label-noise learning,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:11.210953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d9163e34-afc5-44a8-8cbb-1f6cb234000b · outbound

This paper cites Manifold dividemix: A semi-supervised contrastive learning framework for severe label noise,.

Predicting Formula 1 Race Outcomes: Decomposing the Roles of Drivers and Constructors through Linear Modeling Manifold dividemix: A semi-supervised contrastive learning framework for severe label noise,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:11.190575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5b1f0139-7255-4d2b-9dc4-f417aa98eaea · outbound

This paper cites Making deep neural networks robust to label noise: A loss correction approach,.

Predicting Formula 1 Race Outcomes: Decomposing the Roles of Drivers and Constructors through Linear Modeling Making deep neural networks robust to label noise: A loss correction approach,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:11.166031Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4c015154-b4ad-4ad6-992e-0eca3b12cddc · outbound

This paper cites Joint optimization framework for learning with noisy labels,.

Predicting Formula 1 Race Outcomes: Decomposing the Roles of Drivers and Constructors through Linear Modeling Joint optimization framework for learning with noisy labels,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:11.144581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7e06ac62-5775-49e0-a530-6703c364620d · outbound

This paper cites Early-learning regularization: Quieting the confusion in early training,.

Predicting Formula 1 Race Outcomes: Decomposing the Roles of Drivers and Constructors through Linear Modeling Early-learning regularization: Quieting the confusion in early training,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:11.125468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ba32bb13-4316-4707-a436-e142fa889fd3 · outbound

This paper cites Understanding deep learning (still) requires rethinking generalization,.

Predicting Formula 1 Race Outcomes: Decomposing the Roles of Drivers and Constructors through Linear Modeling Understanding deep learning (still) requires rethinking generalization,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:11.094926Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5ab04f6f-f5ef-4945-9e84-9000b2e7e1a8 · outbound

This paper cites Robust loss functions under label noise for deep neural networks,.

Predicting Formula 1 Race Outcomes: Decomposing the Roles of Drivers and Constructors through Linear Modeling Robust loss functions under label noise for deep neural networks,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:11.072926Z

Source-reported events for the cited work

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Observation 8e7f0fdd-f6a4-4454-8d97-57cac0983ece · outbound

This paper cites Generalized cross entropy loss for training deep neural networks with noisy labels,.

Predicting Formula 1 Race Outcomes: Decomposing the Roles of Drivers and Constructors through Linear Modeling Generalized cross entropy loss for training deep neural networks with noisy labels,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:11.053538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 68afb037-336a-4b08-9c48-0e8cff45654e · outbound

This paper cites Symmetric cross entropy for robust learning with noisy labels,.

Predicting Formula 1 Race Outcomes: Decomposing the Roles of Drivers and Constructors through Linear Modeling Symmetric cross entropy for robust learning with noisy labels,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:11.033282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8a916c9d-dd46-4d30-a5d8-bc45431aed7c · outbound

This paper cites Normalized loss functions for learning with noisy labels,.

Predicting Formula 1 Race Outcomes: Decomposing the Roles of Drivers and Constructors through Linear Modeling Normalized loss functions for learning with noisy labels,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:11.008564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fa470f18-2ee8-485f-8ac0-6b0e6b91f0a5 · outbound

This paper cites Asymmetric loss functions for learning with noisy labels,.

Predicting Formula 1 Race Outcomes: Decomposing the Roles of Drivers and Constructors through Linear Modeling Asymmetric loss functions for learning with noisy labels,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:10.989939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4e1c7f2f-1483-43f6-b9e5-3c595eca61bb · outbound

This paper cites Active negative loss: A robust framework for learning with noisy labels,.

Predicting Formula 1 Race Outcomes: Decomposing the Roles of Drivers and Constructors through Linear Modeling Active negative loss: A robust framework for learning with noisy labels,

Reference 19

Resolution: verified exact; raw_fallback, observed 2026-08-06T10:20:10.833521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 44b890a1-c305-410b-bb5c-4aad54e5d3af · outbound

This paper cites An embedding is worth a thousand noisy labels,.

Predicting Formula 1 Race Outcomes: Decomposing the Roles of Drivers and Constructors through Linear Modeling An embedding is worth a thousand noisy labels,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:10.966394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c6f7a2f8-bead-4734-b2a7-2f9359289a83 · outbound

This paper cites Graph construction from data by non-negative kernel regression,.

Predicting Formula 1 Race Outcomes: Decomposing the Roles of Drivers and Constructors through Linear Modeling Graph construction from data by non-negative kernel regression,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:10.947980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a94376dd-f527-44b4-b85b-bfaae8e6145d · outbound

This paper cites Revisiting local neighborhood methods in machine learning,.

Predicting Formula 1 Race Outcomes: Decomposing the Roles of Drivers and Constructors through Linear Modeling Revisiting local neighborhood methods in machine learning,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:10.927545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 11c0fc54-3f3e-45c4-a447-0bc66b19037b · outbound

This paper cites Learning multiple layers of features from tiny images,.

Predicting Formula 1 Race Outcomes: Decomposing the Roles of Drivers and Constructors through Linear Modeling Learning multiple layers of features from tiny images,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T10:20:10.685861Z

Source-reported events for the cited work

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Observation 118011a6-5fa2-4f69-b689-690807fa0721 · outbound

This paper cites Medmnist v2-a large-scale lightweight benchmark for 2d and 3d biomedical image classification,.

Predicting Formula 1 Race Outcomes: Decomposing the Roles of Drivers and Constructors through Linear Modeling Medmnist v2-a large-scale lightweight benchmark for 2d and 3d biomedical image classification,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T10:20:10.692276Z

Source-reported events for the cited work

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Observation 3bd86846-e299-4086-b236-d0886504700d · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Predicting Formula 1 Race Outcomes: Decomposing the Roles of Drivers and Constructors through Linear Modeling DINOv2: Learning Robust Visual Features without Supervision

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T10:20:10.697336Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.