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Record W2306926430 · doi:10.3389/fnhum.2016.00103

Editorial: Music, Brain, and Rehabilitation: Emerging Therapeutic Applications and Potential Neural Mechanisms

2016· editorial· en· W2306926430 on OpenAlexaff
Teppo Särkämö, Eckart Altenmüller, Antoni Rodríguez‐Fornells, Isabelle Peretz

Bibliographic record

VenueFrontiers in Human Neuroscience · 2016
Typeeditorial
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsUniversité de MontréalInternational Laboratory for Brain, Music and Sound ResearchCentre for Research on Brain Language and Music
Fundersnot available
KeywordsCognitive scienceCognitive neuroscienceNeurosciencePsychologyCognitionComputer science

Abstract

fetched live from OpenAlex

(This Editorial is for our Research Topic which was jointly hosted by Frontiers in Human Neuroscience (http://journal.frontiersin.org/researchtopic/1705) and Frontiers in Auditory Cognitive Neuroscience (http://journal.frontiersin.org/researchtopic/1621). We were allowed to write a longer Editorial presenting all the published papers (27 papers in total). The present text is 3000 words, we hope this is ok. The manuscript is enclosed)

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)0.005
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.016
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.017
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.001
Science and technology studies0.0020.002
Scholarly communication0.0050.003
Open science0.0020.001
Research integrity0.0070.010
Insufficient payload (model declined to judge)0.0160.012

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 head0.014
GPT teacher head0.285
Teacher spread0.271 · 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 designNot applicable
Domainnot available
GenreEditorial

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

Citations86
Published2016
Admission routes1
Has abstractyes

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