MétaCan
Menu
Back to cohort
Record W2185903804

Toward a generic approach for stress testing of drug substances and drug products

2005· article· en· W2185903804 on OpenAlexaff
Silke Klick, Pim G. Muijselaar, Joop Waterval, Thomas Eichinger, Christian Korn, Thijs K. Gerding, Alexander J. Debets, Cari Sänger van de Griend, Cas Van Den Beld, Govert W. Somsen, Gerhardus J. de Jong

Bibliographic record

VenueVU Research Portal · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPesticide Residue Analysis and Safety
Canadian institutionsAkzoNobel (Canada)
Fundersnot available
KeywordsDrugProfiling (computer programming)Stress testing (software)Drug developmentReliability engineeringComputer scienceBiochemical engineeringPharmacologyEngineeringMedicine
DOInot available

Abstract

fetched live from OpenAlex

The Impurity Profiling Group has developed a generic approach for conducting stress testing on drug substances and drug products. The proposed strategy is evaluated and verified with historical data and new experiments. Results demonstrate that the proposed approach is reasonable and generates relevant, generally predictive results for the development of a stability-indicating method.

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)0.006
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.006
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.002

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 head0.129
GPT teacher head0.318
Teacher spread0.189 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations91
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueVU Research PortalSame topicPesticide Residue Analysis and SafetyFrench-language works237,207