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Record W2100707968 · doi:10.1002/jemt.20050

The physiology of insect auditory afferents

2004· review· en· W2100707968 on OpenAlex
Andrew C. Mason, Paul A. Faure

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMicroscopy Research and Technique · 2004
Typereview
Languageen
FieldNeuroscience
TopicNeurobiology and Insect Physiology Research
Canadian institutionsThe Scarborough HospitalMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsNeuroscienceBiologyAfferentInsectAuditory systemReceptorPopulationNeural codingMedicineEcology

Abstract

fetched live from OpenAlex

This review presents an overview of the physiology of primary receptors serving tympanal hearing in insects. Auditory receptor responses vary with frequency, intensity, and temporal characteristics of sound stimuli. Various insect species exploit each of these parameters to differing degrees in the neural coding of auditory information, depending on the nature of the relevant stimuli. Frequency analysis depends on selective tuning in individual auditory receptors. In those insect groups that have individually tuned receptors, differences in physiology are correlated with structural differences among receptors and with the anatomical arrangement of receptors within the ear. Intensity coding is through the rate-level characteristics of tonically active auditory receptors and through variation in the absolute sensitivities of individual receptors (range fractionation). Temporal features of acoustic stimuli may be copied directly in the timing of afferent responses. Salient signal characteristics may also be represented by variation in the timing of afferent responses on a finer temporal scale, or by the synchrony of responses across a population of receptors.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.707
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.005
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0000.000

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 head0.152
GPT teacher head0.455
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