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Effects of ambient acidity on chemosensory learning: an example of an environmental constraint on acquired predator recognition in wild juvenile Atlantic salmon (<i>Salmo salar</i>)

2007· article· en· W2014531771 on OpenAlexafffund
Antoine O. H. C. Leduc, Ellie Roh, Cindy Breau, G. E. Brown

Bibliographic record

VenueEcology Of Freshwater Fish · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of New BrunswickConcordia University
FundersConcordia University
KeywordsSalmoPredationPredatorJuvenileALARMBiologyEcologyStimulus (psychology)Fish <Actinopterygii>CommunicationFisheryCognitive psychologyPsychology

Abstract

fetched live from OpenAlex

Abstract – In many prey fish species, learning the identity of novel predators may be facilitated when novel predator cues and an aversive stimulus are presented together. Damage‐released chemical alarm cues are typically released from the skin of prey individuals following mechanical damage and are known to mediate the learned recognition of novel predators. While such chemically mediated acquired predator recognition can provide increased survival benefits to prey, environmental constraints impeding learning may exist. For example, in several fish species the ability to detect chemical alarm cues is impaired in under acidic conditions and as such, inhibits this chemically mediated learning. In this experiment, we studied in two streams of different mean acidity level (pH c . 6.0 and 7.0), to assess if wild juvenile Atlantic salmon ( Salmo salar ) had the ability to acquire the recognition of a novel lemon essence odour when paired with conspecific chemical alarm cues. Our results demonstrate that under acidic conditions, no learned recognition of the novel odour occurred. In neutral conditions, however, salmon recognised the novel odour as a predation threat. This result suggests that ambient acidity creates an environmental constraint on chemically mediated learned recognition of a novel cue.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.121
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.014
GPT teacher head0.211
Teacher spread0.197 · 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

Citations22
Published2007
Admission routes2
Has abstractyes

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