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Record W4255347084 · doi:10.1515/iupac.83.0395

Inhibitory Concentration (IC)

2016· dataset· en· W4255347084 on OpenAlexaff
John Proudfoot, Olivier Nosjean, Jan Blanchard, John Wang, Dominique Besson, Denis Crankshaw, Günter Gauglitz, Robert Hertzberg, Carol Homon, Lyndon Llewellyn, Richard R. Neubig, Larry Walker, Pascal Villa

Bibliographic record

VenueIUPAC Standards Online · 2016
Typedataset
Languageen
FieldChemistry
TopicAnalytical Methods in Pharmaceuticals
Canadian institutionsMcMaster University
Fundersnot available
KeywordsGlossaryContext (archaeology)Field (mathematics)Process (computing)Multidisciplinary approachComputer scienceComponent (thermodynamics)Data scienceManagement scienceEngineeringSociologyBiologyLinguisticsMathematicsSocial science

Abstract

fetched live from OpenAlex

Biomolecular screening is now a crucial component of the drug discovery process, and this glossary will be of use to practitioners in the field of screening and to those who interact with the screening community. The glossary contains definitions related to various aspects of the screening process such as assay types, data handling, and relevant technologies. Many of the terms used in this discipline are not covered by existing glossaries, and where they are, the definitions are often not appropriate for this field. Where appropriate, this document provides new or modified definitions to better reflect the new context. The field of biomolecular screening is multidisciplinary in nature, and this glossary, containing authoritative definitions, will be useful not only for regular practitioners, but also for those who make use of data generated during the screening 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)0.002
metaresearch head score (Gemma)0.011
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 score0.068
Threshold uncertainty score0.226

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0040.006
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0680.070

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 head0.039
GPT teacher head0.480
Teacher spread0.441 · 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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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Same venueIUPAC Standards OnlineSame topicAnalytical Methods in PharmaceuticalsFrench-language works237,207