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Record W6906093786 · doi:10.17035/d.2022.0234944684

Raw EPR spectra and .m matlab files containing simulation code UV-vis data

2022· other· en· W6906093786 on OpenAlexaff

Bibliographic record

VenueFigshare · 2022
Typeother
Languageen
Field
Topic
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsMATLABElectron paramagnetic resonanceHalogenSpectral lineCode (set theory)HalogenationRaw materialField (mathematics)Series (stratigraphy)

Abstract

fetched live from OpenAlex

The α-haloketones are important precursors for synthetic chemistry and pharmaceutical applications; however, their production relies heavily on traditional synthetic methods via halogenation of ketones that are toxic and environmentally risky. By employing NiX2 (X = Cl, Br) as the halogen source, a series of α-haloketones can be synthesized using atmospheric air as the oxidant under visible-light irradiation. Data are: magnetic field units vs spectral intensity, spin system parameters, wavelength units vs spectral intensity.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.385
Threshold uncertainty score0.877

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0040.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.3850.103

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 head0.139
GPT teacher head0.343
Teacher spread0.205 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2022
Admission routes1
Has abstractyes

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