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Record W4232366505 · doi:10.1017/cjn.2014.117

CJN volume 41 issue 6 Cover and Front matter

2014· article· en· W4232366505 on OpenAlexvenueno aff
Michelle Ploughman, Anil Venkitachalam, Stacey Hume, Oksana Suchowersky, Donald T. Stuss, Noreen Kamal, Oscar Benavente, Karl Boyle, Brian Buck, Kenneth Butcher, Leanne K. Casaubon, Robert Côté, Andrew M. Demchuk, Yan Deschaintre, Dar Dowlatshahi, Gordon Gubitz, Gary Hunter, Tom Jeerakathil, Albert Jin, Eddy Lang, Sylvain Lanthier, Patrice Lindsay, Nancy Newcommon, Jennifer Mandzia, Colleen M. Norris, Wes Oczkowski, Céline Odier, Stephen Phillips, Alexandre Y. Poppe, Gustavo Saposnik, Daniel Selchen, Ashfaq Shuaib, Frank L. Silver, Eric E. Smith, Grant Stotts, Michael Suddes, Richard H. Swartz, Philip Teal, Tim Watson, Michael Hill Original, L. Allen, Marina Richardson, A Mcintyre, Shannon Janzen, Michelle N. Meyer, David Ure, Deb Willems, Robert Teasell, Marie-Sarah Gagne, Jean-Martin Boulanger, Nancy Leblanc, Léo Berger, Micheal Benzazon, Trent Orton, Cheemun Lum, Mohammed Alhazzaa, Howard Lesiuk, Marlise dos Santos, Daniela Iancu, André Parent

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicDiverse Scientific and Economic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFront coverFront (military)Cover (algebra)Volume (thermodynamics)Action (physics)Content (measure theory)Environmental scienceComputer scienceMathematicsEngineeringPhysicsMechanical engineeringThermodynamicsMathematical analysis

Abstract

fetched live from OpenAlex

The OBI system. The four pillars of OBI's system drive improvement to individual brain health.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.295
Threshold uncertainty score0.421

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0060.002
Open science0.0010.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.7050.515

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 head0.032
GPT teacher head0.209
Teacher spread0.178 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

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

Citations1
Published2014
Admission routes1
Has abstractyes

Explore more

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