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Record W2069358169 · doi:10.3109/00016480902856562

Off-channel effect of high-frequency overstimulation on duration tuning of low-frequency inferior colliculus neurons in guinea pigs

2009· article· en· W2069358169 on OpenAlexafffund
Zhengnong Chen, Dongzhen Yu, Yanmei Feng, K. X. Su, Jian Wang, Shankai Yin

Bibliographic record

VenueActa Oto-Laryngologica · 2009
Typearticle
Languageen
FieldNeuroscience
TopicHearing, Cochlea, Tinnitus, Genetics
Canadian institutionsDalhousie University
FundersNational Institutes of HealthShanghai Jiao Tong UniversityNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsInferior colliculusStimulationDuration (music)Tone (literature)Guinea pigAudiologyMedicineNeuroscienceBiologyPhysicsInternal medicineAcousticsNucleus

Abstract

fetched live from OpenAlex

CONCLUSION: High-frequency overstimulation can cause the loss of duration selectivity in low-frequency inferior colliculus neurons in guinea pigs. OBJECTIVE: To investigate the effect of high-frequency overstimulation on duration tuning in low-frequency inferior colliculus neurons in guinea pigs. MATERIALS AND METHODS: Duration tuning pattern was recorded by measuring the spikes of single neurons in response to the best frequency (BF) of different durations. The effect of high-frequency overstimulation was verified by comparing the responses before and after the tone exposure. RESULTS: In total, 40 duration-tuned neurons were successfully recorded before and after the tone exposure. After the high-frequency tone trauma, a total of 29 neurons (72.5%) became non-duration-tuned.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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

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 head0.022
GPT teacher head0.269
Teacher spread0.247 · 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 designBench or experimental
Domainnot available
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

Citations0
Published2009
Admission routes2
Has abstractyes

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