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Record W2766084204 · doi:10.3389/fnins.2017.00581

Integration of Descending Command Systems for the Generation of Context-Specific Locomotor Behaviors

2017· review· en· W2766084204 on OpenAlexafffund
Linda Kim, Sandeep Sharma, Simon A. Sharples, Kyle A. Mayr, Charlie H.T. Kwok, Patrick J. Whelan

Bibliographic record

VenueFrontiers in Neuroscience · 2017
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicZebrafish Biomedical Research Applications
Canadian institutionsHotchkiss Brain InstituteUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaCumming School of Medicine, University of CalgaryCanadian Institutes of Health ResearchAlberta InnovatesWings for Life
KeywordsContext (archaeology)BrainstemNeuroscienceCentral pattern generatorNeuronal circuitsComputer scienceSpinal cordBiological neural networkPsychologyBiologyMedicineRhythm

Abstract

fetched live from OpenAlex

Over the past decade there has been a renaissance in our understanding of spinal cord circuits; new technologies are beginning to provide key insights into descending circuits which project onto spinal cord central pattern generators. By integrating work from both the locomotor and animal behavioral fields, we can now examine context-specific control of locomotion, with an emphasis on descending modulation arising from various regions of the brainstem. Here we examine approach and avoidance behaviors and the circuits that lead to the production and arrest of locomotion.

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)0.000
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.002

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 head0.172
GPT teacher head0.402
Teacher spread0.230 · 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
GenreReview

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

Citations73
Published2017
Admission routes2
Has abstractyes

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