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Record W4239994480 · doi:10.1017/s031716710001708x

48th Annual Congress of the Canadian Neurological Sciences Federation

2013· article· en· W4239994480 on OpenAlexaffvenueabout
Martín del Campo, Jeanne Teitelbaum, Draga Jichici, Martin Cloutier, Anne‐Louise Lafontaine, Mike Nicolle, Kristine Chapman, Greg L. Bryan, Young London, J. N. Findlay, Mark Halifax, Mike London, Serge Gauthier, Robert E. London, Mary Connolly, Douglas Calgary, James Toronto, Robert Lee, Robert Winnipeg, Jorge London, Richard Desbiens, David City, Mark Calgary, Hans-Peter Hartung, Germany Michael, Hill Calgary, Jackson Winnipeg, Daniel Ottawa, Suchowersky Edmonton, Bc Brian Toyota Vancouver, Brian Weinshenker, Samuel Wiebe, Karen McKenzie, Nir Lipsman, Peter Giacobbe, A Lozano, Vijay Ramaswamy, Marc Remke, E Bouffet, Christine J. Hawkins, Rowena M. A. Packer, SM Pfister, Andrey Korshunov, T Toronto

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMitochondrial Function and Pathology
Canadian institutionsMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsAction (physics)Political scienceMedicinePsychologyNeuroscienceLibrary scienceComputer sciencePhysics

Abstract

fetched live from OpenAlex

The Canadian Journal of Neurological Sciences is published bimonthly.The annual subscription rate for Individuals (print and online) are: C$178.00 (Canada), C$208.00 (US), C$292.00 (International).Subscription rates for Institutions (print and online) are C$198.00(Canada), C$228.00 (US), C$312.00 (International)."Online Only"-C$160.00(Individual), C$180.00 (Institutional).See www.cjns.orgfor full details including taxes.Single copies C$35.00 each plus C$25.00 shipping and handling.

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)0.002
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 score0.995
Threshold uncertainty score0.960

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.3270.116

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 head0.023
GPT teacher head0.251
Teacher spread0.228 · 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

Citations2
Published2013
Admission routes3
Has abstractyes

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