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Record W4251618045 · doi:10.1017/s0025727300057628

Index

2003· paratext· en· W4251618045 on OpenAlexaff
Amos Arnan, Rudolf Arndt, Eric Gruber von Arni, Lucinda Beier, Belgium Belinsky, Ben Assa, Ben Hydropathic, Eduard Benes, John Bennett, John W. Benton, Marion Berghahn, C. Bérnard, Jean Bernard, William A. Bernhard, Alberto, Peter Cameron, Alphonse Candolle, E. P. Cathcart, Aulus Celsus, De, J. Chamberlain, Chia-Feng Chang, Thomas Chaplin, Nathan Keep, Hastings Kelk, Margaret Kennix, Geoffrey Keynes, Aleksei Khomiakov, Annemarie Kinzelbach, S. Kitasato, Pamela Korsmeyer, Hermann Kossel, Pavel Kovalevskii, Henry Kozlovsky, Richard Von

Bibliographic record

VenueMedical History · 2003
Typeparatext
Languageen
Field
Topic
Canadian institutionsCanadian Armed Forces
Fundersnot available
KeywordsComputer scienceIndex (typography)Information retrievalWorld Wide Web

Abstract

fetched live from OpenAlex

254; theory 251, see also Chinese medical theory Acute Leukemia Task Force 299 Adams, Neil, (rev.)126-7 addiction 533-4, see also alcohol; drugs Africa, migration and mortality 276 air and health 363, 457 Aksakov, Sergei 39 Albucasis 94, 247 Alcazar, Andreas 244 alcohol/alcoholism 30, 31, 37, 531 Allan, David 209 alternative medicine 271-2, see also homoeopathy; hydropathy

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)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.823
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1770.076

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 head0.021
GPT teacher head0.260
Teacher spread0.239 · 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
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

Citations0
Published2003
Admission routes1
Has abstractyes

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Same venueMedical HistoryFrench-language works237,207