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Record W2912753551 · doi:10.1038/s41562-019-0531-8

Opportunities and challenges for a maturing science of consciousness

2019· article· en· W2912753551 on OpenAlexaff
Matthias Michel, Diane M. Beck, Ned Block, Hal Blumenfeld, Richard Brown, David Carmel, Marisa Carrasco, Mazviita Chirimuuta, Marvin M. Chun, Axel Cleeremans, Stanislas Dehaene, Stephen M. Fleming, Chris Frith, Patrick Haggard, Biyu J. He, Cecilia Heyes, Melvyn A. Goodale, Liz Irvine, Mitsuo Kawato, Robert W. Kentridge, Jean-Rémi King, Robert T. Knight, Sid Kouider, Victor A. F. Lamme, Dominique Lamy, Hakwan Lau, Steven Laureys, Joseph E. LeDoux, Ying-Tung Lin, Kayuet Liu, Stephen L. Macknik, Susana Martínez‐Conde, George A. Mashour, Lucía Melloni, Lisa Miracchi, Myrto Mylopoulos, Lionel Naccache, Adrian M. Owen, Richard E. Passingham, Luiz Pessoa, Megan A. K. Peters, Dobromir Rahnev, Tony Ro, David Rosenthal, Yuka Sasaki, Claire Sergent, Guillermo Solovey, Nicholas D. Schiff, Anil K. Seth, Catherine Tallon‐Baudry, Marco Tamietto, Frank Tong, Simon van Gaal, Alexandra Vlassova, Takeo Watanabe, Josh Weisberg, Karen Yan, Masatoshi Yoshida

Bibliographic record

VenueNature Human Behaviour · 2019
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsCarleton UniversityWestern University
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Neurological Disorders and StrokeNational Eye InstituteWellcome Trust
KeywordsMisrepresentationConsciousnessSet (abstract data type)Engineering ethicsField (mathematics)Mental healthInterdisciplinarityPsychologySociologyPublic relationsPolitical scienceSocial sciencePsychiatryComputer scienceNeuroscienceEngineering

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.033
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.033
Threshold uncertainty score0.173

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0330.027
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0040.001
Science and technology studies0.0080.074
Scholarly communication0.0150.035
Open science0.0040.014
Research integrity0.0140.024
Insufficient payload (model declined to judge)0.0170.003

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 head0.160
GPT teacher head0.386
Teacher spread0.227 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreCommentary

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

Citations131
Published2019
Admission routes1
Has abstractno

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