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Pathogen inactivation: making decisions about new technologies – preliminary report of a consensus conference

2007· article· en· W1504505660 on OpenAlexaffabout
Harvey G. Klein, David R. Anderson, Marie‐Josée Bernardi, Ritchard G. Cable, William Carey, Jeffrey S. Hoch, Nancy Robitaille, Marco L.A. Sivilotti, Fiona Smaill

Bibliographic record

VenueVox Sanguinis · 2007
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBlood donation and transfusion practices
Canadian institutionsMcMaster UniversityCentre Hospitalier Universitaire Sainte-JustineQueen's UniversityQueen Elizabeth II Health Sciences CentreUniversité de MontréalSt. Michael's HospitalCentre for Interdisciplinary Research in RehabilitationHealth Sciences Centre
FundersNational Institutes of Health
KeywordsNova scotiaLibrary scienceQueen (butterfly)MedicinePolitical scienceSociologyEthnologyBiologyComputer science

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.174
metaresearch head score (Gemma)0.118
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.994
Threshold uncertainty score0.919

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1740.118
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0040.004
Science and technology studies0.0060.004
Scholarly communication0.0080.007
Open science0.0100.009
Research integrity0.0210.026
Insufficient payload (model declined to judge)0.0050.001

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 head0.051
GPT teacher head0.300
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 designQualitative
Domainnot available
GenreEmpirical

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

Citations22
Published2007
Admission routes2
Has abstractno

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