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Record W1963609107 · doi:10.1016/j.clinph.2014.10.158

Inter-expert and intra-expert reliability in sleep spindle scoring

2014· article· en· W1963609107 on OpenAlexfundno aff
Sabrina Lyngbye Wendt, Peter Welinder, Helge B. D. Sørensen, Paul E. Peppard, Poul Jennum, Pietro Perona, Emmanuel Mignot, Simon C. Warby

Bibliographic record

VenueClinical Neurophysiology · 2014
Typearticle
Languageen
FieldNeuroscience
TopicSleep and Wakefulness Research
Canadian institutionsnot available
FundersNational Center for Advancing Translational SciencesNational Center for Research ResourcesNational Institute of Neurological Disorders and StrokeCanadian Institutes of Health ResearchNational Institutes of HealthNational Heart, Lung, and Blood InstituteBrain and Behavior Research Foundation
KeywordsReliability (semiconductor)Sleep (system call)Computer scienceReliability engineeringPhysical medicine and rehabilitationMedicineEngineeringProgramming languagePhysics

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)0.061
metaresearch head score (Gemma)0.120
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.939
Threshold uncertainty score0.325

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0610.120
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.083
GPT teacher head0.389
Teacher spread0.306 · 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 designObservational
DomainMethods
GenreEmpirical

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

Citations77
Published2014
Admission routes1
Has abstractno

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