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Record W4234271038 · doi:10.3410/f.718008686.793485579

Faculty Opinions recommendation of Hierarchy of orofacial rhythms revealed through whisking and breathing.

2013· dataset· en· W4234271038 on OpenAlexfundno aff
Jorge Gallego

Bibliographic record

VenueFaculty Opinions – Post-Publication Peer Review of the Biomedical Literature · 2013
Typedataset
Languageen
FieldNeuroscience
TopicNeuroscience of respiration and sleep
Canadian institutionsnot available
FundersJapan Society for the Promotion of ScienceCanadian Institutes of Health ResearchNational Institutes of HealthHoward Hughes Medical Institute
KeywordsSniffingWhisking in animalsNeuroscienceBreathingRhythmMedullaFacial musclesBiologyPsychologyAnatomyCommunicationSomatosensory systemMedicineInternal medicine

Abstract

fetched live from OpenAlex

Whisking and sniffing are predominant aspects of exploratory behavior in rodents, yet the neural mechanisms that generate their motor patterns remain largely uncharacterized.We use anatomical, behavioral, electrophysiological, and pharmacological tools to demonstrate that these patterns are coordinated by respiratory centers in the ventral medulla.We delineate a distinct region in the ventral medulla that provides rhythmic input to the facial motoneurons that drive protraction of the vibrissae.Neuronal output from this region is reset at each inspiration by direct input from the preBötzinger complex, such that high frequency sniffing has a one-to-one coordination with whisking while basal respiration is accompanied by intervening whisks that occur between breaths.We conjecture that the respiratory nuclei, which project to other premotor regions for oral and facial control, function as a master clock for behaviors that coordinate with breathing.

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)0.007
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: Dataset · Consensus signal: Dataset
Teacher disagreement score0.058
Threshold uncertainty score0.195

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0050.007
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0580.064

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 head0.059
GPT teacher head0.361
Teacher spread0.303 · 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
GenreDataset

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

Citations0
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueFaculty Opinions – Post-Publication Peer Review of the Biomedical Literature→Same topicNeuroscience of respiration and sleep→French-language works237,207→