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Record W4402050925 · doi:10.21105/joss.06898

exoTEDRF: An EXOplanet Transit and Eclipse DataReduction Framework

2024· article· en· W4402050925 on OpenAlexafffund
Michael Radica

Bibliographic record

VenueThe Journal of Open Source Software · 2024
Typearticle
Languageen
FieldEngineering
TopicAstronomical Observations and Instrumentation
Canadian institutionsUniversité de Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsExoplanetEclipseTransit (satellite)Data reductionReduction (mathematics)Computer sciencePhysicsAstronomyMathematicsData miningEngineeringTransport engineering

Abstract

fetched live from OpenAlex

Since the start of science operations in July 2022, JWST has delivered groundbreaking results on a regular basis.For spectroscopic studies of exoplanet atmospheres the process of extracting robust and reliable atmosphere spectra from the raw JWST observations is critical.Especially as the field pushes to detect the signatures of secondary atmospheres on rocky Earth-like planets it is imperative to ensure that the spectral features that drive atmosphere inferences are robust against the particular choices made during the data reduction process.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Software · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0030.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0150.012

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 head0.024
GPT teacher head0.270
Teacher spread0.246 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreSoftware

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations33
Published2024
Admission routes2
Has abstractyes

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