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Record W4250520256 · doi:10.1017/cjn.2015.402

CJN volume 43 issue 1 Cover and Front matter

2016· article· en· W4250520256 on OpenAlexvenueno aff
Michael Wood, Andy Song, David M. Maslove, Cathy Ferri, Daniel Howes, John Muscedere, Jeff Boyd, Garth M. Bray, Deanna Huggett, J. N. Findlay, Joshua Nisar, Tim E. Darsaut, Pierre Duquette, Paul S. Giacomini, Virender Bhan, Marika Hohol, Robyn Schecter, Frederick A. Zeiler, Alan A. Jackson, Brian Chen, Jehane H. Dagher, Camille Costa, Julie Lamoureux, Élaine de Guise, Mitra Feyz, J. Gordon Boyd, Mohammed Basamh, Antony Robert, Singh Saluja, Judith Marcoux, Maciej K. Janik, Patrick McDonald, Anthony M. Kaufmann, Derek Fewer, Jim Butler, G. Schroeder, Michael West, Jean-François Gagné, Moujahed Labidi, André Turmel, Jai Jai, Shiva Shankar, Gavin Langlands, Steve Doucette, Stephen Phillips, Laura Allen, Amanda Mcintyre, Shannon Janzen, Marina Richardson, Matthew J. Meyer, David Ure, Robert Teasell, Samir Shah, Namrata Shah, R. A. Johnson, Alina Nico West, Narayan Prasad, Leslie W. Ferguson, Ali H. Rajput, Alexander Rajput, Laleh Golestanirad, Behzad Elahi, Simon J. Graham, Sunit Das, Lawrence L. Wald, Adil Bata, Krista Ritchie, Andrea L.O. Hebb, Simon Walling, Sharon Warren, Wonita Janzen, Kenneth G. Warren, Lawrence W. Svenson, Donald Schopflocher, Mathieu Bray, Christina Wolfson, Fraser Moore, Jennifer Uniat, Atay Vural, Rahşan Göçmen, Aslı Kurne, Kader Karlı Oğuz, Mesut Çağrı, Ersin Temuçin, Rana Tan, Edgar Karabudak, Sevim Meinl, Noshin Koenig, Esther Fujiwara, M. John Gill, Christopher Power, Jean K. Mah, Lawrence Korngut, Kirsten M. Fiest, Jonathan Dykeman, Lundy Day, Tamara Pringsheim, Nathalie Jetté, Rodney Li, Pi Shan, Michael Nicolle, K. Ming Chan, Nigel Ashworth, Chris White, Paul Winston, Sean P. Dukelow, Yiqun Mi, Aaron O. Bailey, Aimee Neuroimaging, Ann Ghita, Sergio Wiebe, Jens Fanella, Carrie Wrogemann, Mubeen Daymont, Robert A. Fox, Claudiu Diaconu, Leasa Baus, Alia Grattan, Irene Katzan, Jar-Chi Lee, Larry Raber, Alexander Rae‐Grant, S. Ramesh, Mei Lu, Esther Kim, Cassandra Hawco, Yanfei Wang, Marcia Taylor, Donald F. Weaver, Nikhil Sangle, J. Richard Baringer, Jennifer J. Majersik, L. Dana DeWitt, Andrew Jack, Jian‐Qiang Lu, Robert Ashforth, Robert Broad, Craig Ferguson, David Clarke, Namita Sinha, Anita Florendo-Cumbermack, Dan Selchen, Sarah A. Morrow, Manas Sharma, David A. Steven, Lee Cyn Ang, Courtney Casserly, Jorge G. Burneo, Marcelo Kremenchutzky, Robert Hammond

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicDiverse Scientific and Economic Studies
Canadian institutionsnot available
FundersUniversity of Cambridge
KeywordsFront coverCover (algebra)Front (military)Volume (thermodynamics)Content (measure theory)Action (physics)Computer scienceEnvironmental scienceMathematicsGeographyPhysicsEngineeringMeteorologyMechanical engineering

Abstract

fetched live from OpenAlex

Figure: Correlation plot provides visual representation for the correlation between BtO 2 and other hemodynamic/physiological parameters.Each cell represents the R 2 value for the correlation between BtO 2 and the clinical parameter in the column, for the individual patient in the row.If there is no ellipse, then the correlation coeffi cient is zero.The strength and direction of correlation are dually represented.The increasing strength is represented by the density of the colour as well as by narrowing radius of the ellipse.The direction of the correlation (positive or negative) is represented by the colour of the ellipse (blue = positive correlation; red = negative correlation), as well as its orientation (ellipses angled up and to the right represent positive correlations).The asterisk indicates signifi cant linear correlations (p <0.05).The grey rectangles for patients 1,2,4, and 8 indicate that insuffi cient values were available to calculate the correlation coeffi cient.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.721
Threshold uncertainty score0.398

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0050.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.7210.557

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 head0.036
GPT teacher head0.212
Teacher spread0.177 · 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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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