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Record W4401585090 · doi:10.7554/elife.100211.sa2

Author response: Antibody characterization is critical to enhance reproducibility in biomedical research

2024· peer-review· en· W4401585090 on OpenAlexaff
Richard Kahn, Harvinder Virk, Carl Laflamme, Douglas W. Houston, Nicole K. Polinski, Rob Meijers, Allan I. Levey, Clifford B. Saper, Timothy M. Errington, Rachel E. Turn, Anita Bandrowski, James S. Trimmer, Meghan A. Rego, Leonard P. Freedman, Fortunato Ferrara, Andrew Bradbury, Hannah Cable, Skye Longworth

Bibliographic record

Venuenot available
Typepeer-review
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Biosensing Techniques and Applications
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsReproducibilityCharacterization (materials science)NanotechnologyComputational biologyChemistryMaterials scienceBiologyChromatography

Abstract

fetched live from OpenAlex

There is an ongoing need for researchers and all stakeholders to better understand issues surrounding antibody characterization to improve the quality and reproducibility of research that employs these reagents.

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)0.026
metaresearch head score (Gemma)0.241
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.974
Threshold uncertainty score0.295

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.241
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.003
Scholarly communication0.0060.004
Open science0.0020.004
Research integrity0.0130.014
Insufficient payload (model declined to judge)0.0880.067

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 head0.071
GPT teacher head0.526
Teacher spread0.454 · 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
DomainReproducibility
GenreCommentary

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

Citations2
Published2024
Admission routes1
Has abstractyes

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