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Gustatory responses to feeding‐ and non‐feeding‐stimulant chemicals, with an emphasis on amino acids, in rainbow trout

2006· article· en· W1974126513 on OpenAlexaff
Shinji Yamashita, Taeko Yamada, Toshiaki HARA

Bibliographic record

VenueJournal of Fish Biology · 2006
Typearticle
Languageen
FieldNursing
TopicBiochemical Analysis and Sensing Techniques
Canadian institutionsUniversity of ManitobaFisheries and Oceans Canada
Fundersnot available
KeywordsRainbow troutAmino acidBiologyTasteTaste receptorAlanineReceptorOdorBiochemistryFood scienceFish <Actinopterygii>FisheryNeuroscience

Abstract

fetched live from OpenAlex

Specific receptor and fibre types of rainbow trout Oncorhynchus mykiss involved in the detection and discrimination of amino acids and a heterogeneous collection of compounds were investigated by recording the electrical activity of the maxillary branch of the facial nerve innervating taste buds inside the upper jaw. Proline (Pro), alanine (Ala), leucine (Leu), betaine (Bet) and 2‐amino‐3‐guanidinopropionic acid (Agp) were the major amino acids detected by the gustatory system. The two experimental approaches, concentration‐response curves and cross‐adaptations, showed that all amino acids were detected by three independent receptor types: Pro ‐, Agp/Bet ‐ and Leu ‐receptors. Bile acids, the most potent stimulants recorded, were detected by a single receptor type independent of those for amino acids, with threshold concentrations of 10 −12 M. Strychnine, quinine and tetrodotoxin may have partially shared a single receptor mechanism. The gustatory sensibility narrowly tuned towards the amino acid spectrum compared to those for a diverse array of non‐feeding stimulant chemicals, combined with feeding behaviour triggered primarily by vision and olfaction, suggest that in rainbow trout, and possibly other salmonid species, gustatory chemical cues, in addition to food finding and intake, play an important role in detecting poisonous prey and substances.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.173
Threshold uncertainty score0.510

Codex and Gemma teacher scores by category

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 integrity0.0000.000
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 head0.014
GPT teacher head0.282
Teacher spread0.267 · 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 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

Citations56
Published2006
Admission routes1
Has abstractyes

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