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Record W4240650076 · doi:10.1017/cjn.2018.344

CJN volume 45 issue 5 Cover and Front matter

2018· article· en· W4240650076 on OpenAlexaffvenue
Nens van Alfen, Jean K. Mah, Jodie Burton, Anthony Traboulsee, Mark S. Freedman, Daniel Selchen, Alexandre Prat, Paul S. Giacomini, Craig Campbell, Kathryn Selby, Hugh J. McMillan, Jiri Vajsar, Lawrence Korngut, Bernard Brais, Alex MacKenzie, Maryam Oskoui, Ryan T. Lewinson, Aravind Ganesh, Michael Yeung, Helen Wong, Luke Hodgson, Jillian C. Banfield, Jai Shankar, Sneha Chenji, Dennell Mah, Wendy Johnston, Richard Camicioli, Nancy Fisher, Sanjay Kalra, Abdullah Ishaque, Rouzbeh Maani, Jerome Satkunam, Peter Seres, Alan H. Wilman, Sandeep Naik, Yee‐Hong Yang, Julien Hébert, Gregory S. Day, Claude Steriade, Richard Wennberg, David F. Tang‐Wai, Michael D. Staudt, Alexander J. Clark, Allan Gordon, Mary Lynch, Pat Morley-Forster, Howard Nathan, Catherine Smyth, Larry Stitt, Cory Toth, Mark A. Ware, Dwight E. Moulin, Christopher J. Honey, Armaan K. Malhotra, Mandeep S. Tamber, Michel Prudhomme, Ivar Mendez, Qiongqiong Li, Cheng Wang, Huidong Tang, Shengdi Chen, Jianfang Ma, Neuroimaging Highlights, John M. Gorman, Nikkie Randhawa, Daniel Mendelsohn, Manraj K. S. Heran, Silke Appel‐Cresswell, Robert Toronto, Robert London, P Montreal, Hans Katzberg, A Mañas, Sharma London, Jeanne Montreal, Richard Toronto, Greg L. Bryan, Young London, Douglas Calgary, James Toronto, Robert Lee, Robert Winnipeg, Jorge G. Burneo, Colin Chalk, King Ming Chan, Alan Goodridge, Nl S John's, Mark G. Hamilton, Michael D. Hill, Alan A. Jackson, Draga Jichici, Suneil K. Kalia, Daniel Keene, Julia Keith, Stephen Lownie, Jian‐Qiang Lu, Patrick McDonald, Joseph Megyesi, Tiago Mestre, Sarah A. Morrow, Michael Nicolle, Narayan Prasad, Alex Rajput, Kesh Reddy, Ramesh Sahpaul, Dipanka Sarma, Sean Symons, Brian Toyota, Brian G. Weinshenker, Sam Wiebe, Eugene Yu

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicDiverse Scientific and Economic Studies
Canadian institutionsNovartis (Canada)
Fundersnot available
KeywordsFront coverFront (military)Cover (algebra)Volume (thermodynamics)Content (measure theory)Action (physics)Environmental scienceComputer scienceGeographyMathematicsEngineeringPhysicsMeteorologyMechanical engineering

Abstract

fetched live from OpenAlex

We are committed to advancing the science of migraines.Today, a growing understanding of CGRP* brings new insights to the science behind the migraine experience.

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 score0.192
Threshold uncertainty score0.274

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0020.001
Scholarly communication0.0060.001
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.8080.659

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 head0.037
GPT teacher head0.220
Teacher spread0.183 · 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

Citations0
Published2018
Admission routes2
Has abstractyes

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