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Record W4394448047 · doi:10.6084/m9.figshare.21077615

Additional file 1 of Comprehensive chemical profiling of volatile constituents of Angong Niuhuang Pill in vitro and in vivo based on gas chromatography coupled with mass spectrometry

2022· dataset· en· W4394448047 on OpenAlexaff
Yue Jiang, Jie Li, Meng Ding, Zifan Guo, Hua Yang, Hui‐Jun Li, Wen Gao, Ping Li

Bibliographic record

VenueOpen MIND · 2022
Typedataset
Languageen
FieldMedicine
TopicTraditional Chinese Medicine Analysis
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsChromatographyChemistryGas chromatography–mass spectrometryMass spectrometryPillIn vivoProfiling (computer programming)PharmacologyBiotechnologyMedicineComputer scienceBiology

Abstract

fetched live from OpenAlex

Additional file 1: Table S1. The information on eight batches of commercially available ANP samples. Table S2. Dynamic multiple reaction monitoring parameters of all analytes and internal standard. Table S3. Calibration curves, LODs and LOQs of 21 volatile analytes in ANP samples. Table S4. Intra-day precision, inter-day precision, repeatability, stability and recovery of 21 volatile analytes in ANP samples. Table S5. Contents of 21 volatile analytes in ANP samples (μg/g, mean ± SD, n = 4).

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)0.002
metaresearch head score (Gemma)0.012
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 score0.410
Threshold uncertainty score0.842

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.4100.094

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 head0.017
GPT teacher head0.265
Teacher spread0.249 · 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
Published2022
Admission routes1
Has abstractyes

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