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Record W2051907414 · doi:10.1086/668636

SEDfit: Software for Spectral Energy Distribution Fitting of Photometric Data

2012· article· en· W2051907414 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePublications of the Astronomical Society of the Pacific · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicRemote Sensing in Agriculture
Canadian institutionsSaint Mary's University
Fundersnot available
KeywordsSpectral energy distributionSoftwareCurve fittingSoftware packageComputer scienceEnergy (signal processing)AlgorithmEnergy distributionRedshiftMaximum likelihoodAstrophysicsStatistical physicsApplied mathematicsPhysicsMathematicsStatisticsMachine learningGalaxy

Abstract

fetched live from OpenAlex

This paper describes SEDfit, the earliest --- but continually upgraded --- software package for spectral energy distribution fitting (SED fitting) of high-redshift photometric data, and the only one to properly treat non-detections. The principles of maximum-likelihood SED fitting are described, including formulae used for fitting both detected and un-detected (upper limits) photometric data. The internal mechanics of the SEDfit package are presented and several illustrative examples of its use are given. The paper concludes with a discussion of several issues and caveats applicable to SED-fitting in general.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.240

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.229
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it