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Record W2342644652 · doi:10.1016/j.jprot.2016.04.042

Data Independent Acquisition analysis in ProHits 4.0

2016· article· en· W2342644652 on OpenAlexafffund
Guomin Liu, James D.R. Knight, Yanping Zhang, Chih‐Chiang Tsou, Jian Wang, Jean‐Philippe Lambert, Brett Larsen, Mike Tyers, Brian Raught, Nuno Bandeira, Alexey I. Nesvizhskii, Hyungwon Choi, Anne‐Claude Gingras

Bibliographic record

VenueJournal of Proteomics · 2016
Typearticle
Languageen
FieldChemistry
TopicAdvanced Proteomics Techniques and Applications
Canadian institutionsUniversity of TorontoOntario Institute for Cancer ResearchInstitute for Research in Immunology and CancerSinai Health SystemUniversité de MontréalLunenfeld-Tanenbaum Research Institute
FundersNational Center for Research ResourcesNational Institute of General Medical SciencesGenome CanadaCanadian Institutes of Health ResearchNational Institutes of HealthGovernment of CanadaMinistry of Education - Singapore
KeywordsComputer scienceData 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)0.005
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.104
Threshold uncertainty score0.348

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0050.002
Open science0.0040.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.1040.038

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 head0.027
GPT teacher head0.312
Teacher spread0.286 · 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 designBench or experimental
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

Citations82
Published2016
Admission routes2
Has abstractno

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