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CJN volume 47 issue 1 Cover and Front matter
Garima Shukla, Anupama Gupta, Kamalesh Chakravarty, Angela Ann Joseph, Aathira Ravindranath, Minesh P. Mehta, Sheffali Gulati, Madhulika Kabra, Mohammed Afsar, Shivani Poornima, Gauruv Bose, Simon Thebault, Harold Atkins, Mark S. Freedman, Karl Grenier, Fallon Ponnambalam, David E. Lee, Rebecca Lauwers, Shree Bhalerao, Nervous System, Hemangiopericytoma Cohen-Inbar, Stephen Joza, Richard Camicioli, Fang Ba, Sherry Hu, Karim Virani, Jai Shankar, Christine Christian, Kara Matheson, Huiling Cao, Wendy Simpkin, Stephen Phillips, Prosper Koto, David A. Dyment, Asuri N. Prasad, Kym M. Boycott, Grace U. Ediae, Taila Hartley, Ayman Hassan, Katherine Muir, Murray A. Potter, Lysa Boissé Lomax, Olga Jarinova, Bekim Sadiković, Dimitri J. Stavropoulos, O. Carter Snead, John T. Brooks, Melanie L. Yarbrough, Robert C. Bucelli, Gregory S. Day, Cecilia Camarda, Rosolino Camarda, Carmela Pipia, Delia Azzarello, Emanuele Grassedonio, Gianluca Sottile, Giovanna Cilluffo, Paola Torelli, Jayson L. Co, Marcus Vinícius, de Moraes, Rita Katznelson, Amy Evans, David Shultz, Normand Laperrière, Barbara‐Ann Millar, Alejandro Berlín, Paul Kongkham, Derek S. Tsang, A. Dessa Sadovnick, Robert Carruthers, Maria K. Houtchens, Alice Schabas, Penelope Neuroimaging, Matthew Quinn, Lulu Bursztyn, Manas Sharma, Mohamed Réda Bensaïdane, Marie‐Christine Camden, Martin Savard, Bhranavi Arulratnam, Daniel M. Mandell, Jonathan A. Micieli, Eduardo Portela de Oliveira, John Woulfe, Carlos Torres, Nader Zakhari, Paolo Spinnato, Federico Ponti, Silvia de Pasqua, Brenda Varriano, Shaleen Sulway, Curtis Wetmore, Wanda Dillon, Karen Misquitta, Namita Multani, Cassandra Jessica Anor, María Martínez, Elena Cacchione, John Rutka, Maria Carmela Tartaglia, Nicola Rifino, Marco Filizzolo, Davide Sangalli, Paola Basilico, Vittorio Mantero, Chiara Scaccabarozzi, Andrea Salmaggi, Ashkan Shoamanesh, Christine Hawkes, Andreas Charidimou, Mukul Sharma, Kelvin Ng, Anri Hattori, Taiji Tsunemi, Tomoyo Shimada, Emi Nakagawa, Nobutaka Hattori, David J. Schulz, Sachin Pandey, Henry Liu, Edward Margolin, Nataly Pesin, Cindy Lo Kuen Lam, Shino Magaki, Ravi Raghavan, Tanya Minasian, Kenneth De, Los Reyes, Thomas J. Kelly, Shadi Farzin-Gohar, Jeremy Deisch, Ali Cheema, Kristen Mathias, Christine Bui, Steven Dunham, J. Clay Goodman, Hana M. El Sahly, Robert Chen, Robert Hammond, Philippe Hout, Mahendranath Moharir, Tejas Sankar, Jeanne Teitelbaum, Richard Wennberg, Greg L. Bryan, Young London, Douglas W. Zochodne, James Sharpe, Robert Lee, Robert Ross, Jorge G. Burneo, Jodie Burton, Colin Chalk, King Ming Chan, Alan Goodridge, Mark G. Hamilton, Michael D. Hill, Alan Jackson, Draga Jichici, Suneil K. Kalia, Daniel Keene, Julia Keith, Nir Lipsman, Stephen Lownie, Jian‐Qiang Lu, Patrick McDonald, Joseph Megyesi, Tiago Mestre, Sarah A. Morrow, Michael Nicolle, Ian F. Parney, Narayan Prasad, Alex Rajput, Kesh Reddy, Lawrence R. Robinson, Ramesh Sahpaul, Dipanka Sarma, Madeleine Sharpe, Sean Symons, Brian G. Weinshenker, Sam Wiebe, Jefferson R. Wilson
Bibliographic record
VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicDiverse Scientific and Economic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFront coverCover (algebra)Front (military)Volume (thermodynamics)Content (measure theory)Action (physics)Computer scienceEnvironmental scienceMathematicsGeographyEngineeringPhysicsMeteorologyMechanical engineering
Abstract
OpenAlex records an abstract for this work, but it could not be fetched just now.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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 score0.178
Threshold uncertainty score0.254
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.822 | 0.695 |
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 head0.030
GPT teacher head0.209
Teacher spread0.178 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine 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".
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Citations0
Published2019
Admission routes1
Has abstractyes