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Record W6977552016 · doi:10.6084/m9.figshare.961790.v2

Spectral indices for J1207-3900 and J1247-3816

2014· dataset· en· W6977552016 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2014
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsPython (programming language)Link (geometry)Spectral analysisSpectral propertiesR package

Abstract

fetched live from OpenAlex

These IDL save files each contain a structure, grouping all spectral indices from several bibliographical references. There is one IDL savefile per spectrum. You can find all spectra in the Montreal Spectral Library (1st link below). The data can be restored into IDL with the following example command : IDL> restore, 'J1247-3816_SpeX_LowRes15_spectral_indices.sav', /verbose It is also possible to restore IDL save files in Python (see 2nd link below on how to do this) Please cite Gagné et al. 2014b (3rd link below) as well as corresponding references in the IDL structure tag names, when you use any of these informations.

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)0.001
metaresearch head score (Gemma)0.004
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: Dataset · Consensus signal: Dataset
Teacher disagreement score0.123
Threshold uncertainty score0.411

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0070.009
Science and technology studies0.0020.001
Scholarly communication0.0040.003
Open science0.0030.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.1230.226

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 head0.057
GPT teacher head0.315
Teacher spread0.258 · 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
GenreDataset

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".

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Citations0
Published2014
Admission routes1
Has abstractyes

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