Paper Citation Record · LEDGER

Why Machine Learning Models Systematically Underestimate Extreme Values

As of 14 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 2 inbound Pith citation observations for arXiv:2412.05806.

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

pith.paper-citation-record.v1
2412.05806 v2

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T20:24:30.931745Z

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:14:08.574687Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-10T05:30:23.456663Z

Reference resolution

17 of 17 outbound references displayed

verified exact8
verified fuzzy0
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

1
pith, observed 2026-08-10T05:30:23.456663Z

Outbound references

Observation 5df2dc7d-ce44-4bf4-b629-9ec7b34b45e6 · outbound

This paper cites Galactic Stellar Populations in the Era of SDSS and Other Large Surveys.

Why Machine Learning Models Systematically Underestimate Extreme Values Galactic Stellar Populations in the Era of SDSS and Other Large Surveys

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-11T20:24:30.846769Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e228fda9-5219-4ccc-b3b6-3b9fec95035d · outbound

This paper cites Science-Driven Optimization of the LSST Observing Strategy.

Why Machine Learning Models Systematically Underestimate Extreme Values Science-Driven Optimization of the LSST Observing Strategy

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-11T20:24:30.852608Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 235f18f7-d417-4c87-9237-ff3be846035b · outbound

This paper cites The Data Reduction Pipeline for the Apache Point Observatory Galactic Evolution Experiment.

Why Machine Learning Models Systematically Underestimate Extreme Values The Data Reduction Pipeline for the Apache Point Observatory Galactic Evolution Experiment

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-08-11T20:24:31.411059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3d49e5c9-4e7a-48b1-af09-a49c0571bbbf · outbound

This paper cites The Apache Point Observatory Galactic Evolution Experiment (APOGEE).

Why Machine Learning Models Systematically Underestimate Extreme Values The Apache Point Observatory Galactic Evolution Experiment (APOGEE)

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T20:24:30.864185Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2d23ac9-b145-4fc0-85a7-922057184f2a · outbound

This paper cites The Apache Point Observatory Galactic Evolution Experiment (APOGEE) Spectrographs.

Why Machine Learning Models Systematically Underestimate Extreme Values The Apache Point Observatory Galactic Evolution Experiment (APOGEE) Spectrographs

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T20:24:30.869368Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e170d928-0b2b-4e0d-99ba-6dc411ae7f76 · outbound

This paper cites The Stellar Content of Active Galaxies.

Why Machine Learning Models Systematically Underestimate Extreme Values The Stellar Content of Active Galaxies

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-08-11T20:24:31.350725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation aaf1ce8b-d329-4c82-bd28-db5f00e6eb30 · outbound

This paper cites Semi-empirical analysis of SDSS galaxies: I. Spectral synthesis method.

Why Machine Learning Models Systematically Underestimate Extreme Values Semi-empirical analysis of SDSS galaxies: I. Spectral synthesis method

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T20:24:30.880175Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 67eeba45-e8af-4b5b-b3a4-daf486e3debd · outbound

This paper cites Wang, H.-T.

Why Machine Learning Models Systematically Underestimate Extreme Values Wang, H.-T

Reference 8

Resolution: verified exact; raw_fallback, observed 2026-08-11T20:24:31.307707Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a660eec1-8d48-4664-9d65-b0956976f568 · outbound

This paper cites The First Data Release of LAMOST Low Resolution Single Epoch Spectra.

Why Machine Learning Models Systematically Underestimate Extreme Values The First Data Release of LAMOST Low Resolution Single Epoch Spectra

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-08-11T20:24:31.150552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation df708bb3-e1f2-49db-ac74-e7c707610618 · outbound

This paper cites Overview of the DESI Milky Way Survey.

Why Machine Learning Models Systematically Underestimate Extreme Values Overview of the DESI Milky Way Survey

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T20:24:30.895154Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 360e5ec9-1354-4a8b-bbc3-f033f6270c39 · outbound

This paper cites The Sloan Digital Sky Survey Reverberation Mapping Project: The Black Hole Mass$-$Stellar Mass Relations at $0.2\lesssim z\lesssim 0.8$.

Why Machine Learning Models Systematically Underestimate Extreme Values The Sloan Digital Sky Survey Reverberation Mapping Project: The Black Hole Mass$-$Stellar Mass Relations at $0.2\lesssim z\lesssim 0.8$

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T20:24:30.900272Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 135fcf39-0ded-46f3-bf85-253b612511c4 · outbound

This paper cites Spectroscopic failures in photometric redshift calibration: cosmological biases and survey requirements.

Why Machine Learning Models Systematically Underestimate Extreme Values Spectroscopic failures in photometric redshift calibration: cosmological biases and survey requirements

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-11T20:24:31.088177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 12e16c0f-9cef-4dbd-a722-3c5c13ff5317 · outbound

This paper cites Self-calibration and robust propagation of photometric redshift distribution uncertainties in weak gravitational lensing.

Why Machine Learning Models Systematically Underestimate Extreme Values Self-calibration and robust propagation of photometric redshift distribution uncertainties in weak gravitational lensing

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-11T20:24:31.064139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ace0e220-b213-4afb-a32e-fb9e6eb76513 · outbound

This paper cites Organised Randoms: Learning and correcting for systematic galaxy clustering patterns in KiDS using self-organising maps.

Why Machine Learning Models Systematically Underestimate Extreme Values Organised Randoms: Learning and correcting for systematic galaxy clustering patterns in KiDS using self-organising maps

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-11T20:24:31.041091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a3b2c828-49e0-475a-8efc-b8dfdb5efe76 · outbound

This paper cites J-PLUS: Support Vector Regression to Measure Stellar Parameters.

Why Machine Learning Models Systematically Underestimate Extreme Values J-PLUS: Support Vector Regression to Measure Stellar Parameters

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-08-11T20:24:31.016188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1146fd6c-d1b5-4b2e-b149-7c6d2c8ba5da · outbound

This paper cites The Southern Photometric Local Universe Survey (S-PLUS): improved SEDs, morphologies and redshifts with 12 optical filters.

Why Machine Learning Models Systematically Underestimate Extreme Values The Southern Photometric Local Universe Survey (S-PLUS): improved SEDs, morphologies and redshifts with 12 optical filters

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T20:24:30.926934Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d922b493-3391-480d-a8eb-0de97e447878 · outbound

This paper cites The miniJPAS survey: a preview of the Universe in 56 colours.

Why Machine Learning Models Systematically Underestimate Extreme Values The miniJPAS survey: a preview of the Universe in 56 colours

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T20:24:30.931745Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation f6f9a20a-55f6-4126-bd1e-75bf46c84872 · inbound

New Rotation Periods from the Kepler Bonus Background Light Curves cites this paper.

New Rotation Periods from the Kepler Bonus Background Light Curves Why Machine Learning Models Systematically Underestimate Extreme Values

Reference 56

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T11:14:09.354191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d58c0df4-32d4-4284-bd66-85d87c6c37d9 · inbound

BOSS-CLAM: Utilizing a Constrained Linear Absorption Model to Infer Stellar Parameters from BOSS Spectra cites this paper.

BOSS-CLAM: Utilizing a Constrained Linear Absorption Model to Infer Stellar Parameters from BOSS Spectra Why Machine Learning Models Systematically Underestimate Extreme Values

Reference 162

Resolution: unresolved; no resolver link, observed 2026-08-01T04:28:48.797094Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.