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Record W1757755710 · doi:10.1080/14992020600803869

The effect of gap-marker spectrum on gap-evoked auditory response from the inferior colliculus and auditory cortex of guinea pigs

2006· article· en· W1757755710 on OpenAlexafffund
Jian Wang, Yanmei Fenga, Shankai Yin

Bibliographic record

VenueInternational Journal of Audiology · 2006
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsInferior colliculusAuditory cortexAudiologyEvoked potentialBandwidth (computing)PhysicsNeuroscienceMedicinePsychologyTelecommunicationsComputer science

Abstract

fetched live from OpenAlex

The objective of this study is to verify the effects of gap marker spectrum on gap-evoked auditory responses. The gap-evoked potentials were recorded using electrodes implanted in the inferior colliculus (IC) and auditory cortex (AC) of guinea pigs. The gap markers were noise bursts in four frequency bands (500-8,000 Hz, 500-16,000 Hz, 500-32,000 Hz, and 16,000-32,000 Hz), and were tested at three sound levels. The onset response to the post-gap marker was measured to obtain the gap response threshold, and to establish input-output functions for latency and amplitude. Similar to previous behavioural studies, it was found that the gap-response threshold decreased with increasing marker bandwidth. This change was more significant at the cortical level in which the averaged gap-threshold decreased by approximately 2 ms with the bandwidth change. However, the gap threshold in the high frequency region (16,000-32,000 Hz) was comparable to that of the low frequency region (500-16,000 Hz). These results suggest that the total bandwidth of all auditory channels that are recruited determine the temporal resolution measured in gap-evoked potentials.

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 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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.907
Threshold uncertainty score0.523

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.010
GPT teacher head0.276
Teacher spread0.265 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations6
Published2006
Admission routes2
Has abstractyes

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