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Record W4243581145 · doi:10.16889/isomerdesign-5-supp

PeakAL: Protons I Have Known and Loved, Too — Another Fifty Shades of Grey-Market Spectra. Supplementary Data

2017· report· en· W4243581145 on OpenAlexafffund
Stephen J. Chapman

Bibliographic record

Venuenot available
Typereport
Languageen
FieldMaterials Science
TopicMachine Learning in Materials Science
Canadian institutionsIsomer Design (Canada)
FundersUniversity of Toronto
KeywordsTryptaminesBenzofuranChemistrySpectral lineNMR spectra databaseIndole testStereochemistryPhysicsTryptamine

Abstract

fetched live from OpenAlex

1H NMR spectra of 43 alleged psychedelic tryptamines (2-(1H-indol-3-yl)ethan-1-amines) and benzofuran analogues (2-(1-benzofuran-3-yl)ethan-1-amines) from grey-market internet vendors across North America and Europe were acquired and compared. Tryptamines having mono- or di- alkyl, cyclopropyl, or allyl amine substituents and/or substitution of the indole/benzofuran ring by acetyloxy, hydroxy, methoxy, or methyl groups were analysed. 1H NMR spectra for some of these compounds have not been previously reported to my knowledge. The spectrum of each analyte is consistent with the compound as labelled and sold. None of the analytes was found to be unequivocally misrepresented. This collection of experimentally uniform spectra may help forensic and harm-reduction organizations identify these compounds, some of which appear only sporadically. The complete spectra are provided as supplementary data.

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)0.002
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: Dataset · Consensus signal: Dataset
Teacher disagreement score0.336
Threshold uncertainty score0.947

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.3360.043

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 head0.070
GPT teacher head0.360
Teacher spread0.290 · 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
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".

Quick stats

Citations0
Published2017
Admission routes2
Has abstractyes

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Same topicMachine Learning in Materials ScienceFrench-language works237,207