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Record W4244734507 · doi:10.1385/1592599400

Fibrosis Research

2005· book· es· W4244734507 on OpenAlexfundno aff
John Varga, David A. Brenner, Sem H. Phan

Bibliographic record

VenueHumana Press eBooks · 2005
Typebook
Languagees
FieldMedicine
TopicBiotechnology and Related Fields
Canadian institutionsnot available
FundersDavid Geffen School of Medicine, University of California, Los AngelesMedical Research CouncilSchool of Medicine, New York UniversityEmory UniversityUniversity of RochesterUniversity College LondonUniversity of SouthamptonUniversität RegensburgSchool of Medicine, Boston UniversityUniversity of New South WalesYork UniversitySchool of Medicine, Emory UniversityUniversity of California, Los AngelesState University of New YorkInstitut National de la Santé et de la Recherche MédicaleMedical School, University of MichiganNorthwestern UniversityMedical Center, University of PittsburghUniversity of Pittsburgh
KeywordsMedicineEngineering ethicsField (mathematics)Engineering

Abstract

fetched live from OpenAlex

Leading investigators review the highlights of current fibrosis research and the experimental methodologies used uncover the mechanisms that drive it. In their discussion of research methodologies uti

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)0.003
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.030
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0300.015

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 head0.145
GPT teacher head0.374
Teacher spread0.229 · 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
GenreOther

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

Citations16
Published2005
Admission routes1
Has abstractyes

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