Paper Citation Record · LEDGER

Assessing model calibration with boosting trees

As of 20 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2606.08084.

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

pith.paper-citation-record.v1
2606.08084 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

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measured 33 of 33 standing notices

One-hop event checks from named stored sources.

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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

33 of 33 outbound references displayed

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External citation measurements

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Outbound references

Observation 805a51fe-f517-457d-9e07-391134284738 · outbound

This paper cites an unresolved cited work.

Assessing model calibration with boosting trees Unresolved cited work

Reference 1

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This paper cites an unresolved cited work.

Assessing model calibration with boosting trees Unresolved cited work

Reference 2

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Assessing model calibration with boosting trees Unresolved cited work

Reference 3

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Assessing model calibration with boosting trees Unresolved cited work

Reference 4

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This paper cites an unresolved cited work.

Assessing model calibration with boosting trees Unresolved cited work

Reference 5

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This paper cites an unresolved cited work.

Assessing model calibration with boosting trees Unresolved cited work

Reference 6

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Assessing model calibration with boosting trees Unresolved cited work

Reference 7

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This paper cites (2018).CASdatasetsRpackage vignette.Reference manual.

Assessing model calibration with boosting trees (2018).CASdatasetsRpackage vignette.Reference manual

Reference 8

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Observation d0eedd72-6b2e-463a-96a3-2570fe19233a · outbound

This paper cites Model Comparison and Calibration Assessment: User Guide for Consistent Scoring Functions in Machine Learning and Actuarial Practice.

Assessing model calibration with boosting trees Model Comparison and Calibration Assessment: User Guide for Consistent Scoring Functions in Machine Learning and Actuarial Practice

Reference 9

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Observation 5bdd33d3-1474-4038-b8ce-2e6b1f1059c5 · outbound

This paper cites an unresolved cited work.

Assessing model calibration with boosting trees Unresolved cited work

Reference 10

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Assessing model calibration with boosting trees Unresolved cited work

Reference 11

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This paper cites an unresolved cited work.

Assessing model calibration with boosting trees Unresolved cited work

Reference 12

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This paper cites an unresolved cited work.

Assessing model calibration with boosting trees Unresolved cited work

Reference 13

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This paper cites an unresolved cited work.

Assessing model calibration with boosting trees Unresolved cited work

Reference 14

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Observation a5ea4acb-b9b4-4475-aefa-5c9e1bd2784c · outbound

This paper cites an unresolved cited work.

Assessing model calibration with boosting trees Unresolved cited work

Reference 15

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Observation c513e55a-da48-486d-a0eb-ca8e10d2cfe3 · outbound

This paper cites Data Mining, Inference, and Prediction.

Assessing model calibration with boosting trees Data Mining, Inference, and Prediction

Reference 16

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Observation 69cfad1b-28cc-4a38-b708-bc2e13c4dae2 · outbound

This paper cites an unresolved cited work.

Assessing model calibration with boosting trees Unresolved cited work

Reference 17

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Assessing model calibration with boosting trees W., Lemeshow, S

Reference 18

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This paper cites (2004).Probability essentials.2nd ed.

Assessing model calibration with boosting trees (2004).Probability essentials.2nd ed

Reference 19

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Assessing model calibration with boosting trees Unresolved cited work

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Assessing model calibration with boosting trees 2020 , publisher =

Reference 21

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Assessing model calibration with boosting trees Unresolved cited work

Reference 22

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This paper cites H., Winkler, R.

Assessing model calibration with boosting trees H., Winkler, R

Reference 23

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This paper cites The Murphy Decomposition and the Calibration-Resolution Principle: A New Perspective on Forecast Evaluation.

Assessing model calibration with boosting trees The Murphy Decomposition and the Calibration-Resolution Principle: A New Perspective on Forecast Evaluation

Reference 24

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Assessing model calibration with boosting trees Unresolved cited work

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Assessing model calibration with boosting trees Unresolved cited work

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Assessing model calibration with boosting trees Unresolved cited work

Reference 27

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Assessing model calibration with boosting trees Unresolved cited work

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Assessing model calibration with boosting trees Unresolved cited work

Reference 29

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This paper cites (2023).Statistical Foundations of Actuarial Learning and its Applica- tions.Springer Actuarial.https://link.springer.com/book/10.1007/978-3-031-12409-9 15.

Assessing model calibration with boosting trees (2023).Statistical Foundations of Actuarial Learning and its Applica- tions.Springer Actuarial.https://link.springer.com/book/10.1007/978-3-031-12409-9 15

Reference 30

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Assessing model calibration with boosting trees Unresolved cited work

Reference 31

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Assessing model calibration with boosting trees Unresolved cited work

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Assessing model calibration with boosting trees This can be explained by two different factors

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Pith citing papers

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