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Record W3141706478 · doi:10.33137/jns.v2i1.34652

Effect of Potential Sea Lamprey (Petromyzon marinus) Repellents on the Distribution of Juvenile Rainbow Trout (Oncorhynchus mykiss) in a Laboratory Environment

2021· article· en· W3141706478 on OpenAlexaffvenue
Gadfly Stratton, I. Imre, Richard Di Rocco, G. E. Brown

Bibliographic record

VenueUTSC s Journal of Natural Sciences · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsConcordia UniversityAlgoma UniversityThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsPetromyzonRainbow troutBiologyLampreyPredatorZoologyTroutFisheryJuvenileALARMEcologyPredationFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Recent studies have begun to consider the use of chemosensory alarm cues as potential repellents of invasive Sea Lamprey (Petromyzon marinus Linnaeus, 1758) in the Great Lakes Basin. An important factor in determining the efficacy of potential P. marinus repellents is whether they are species-specific. To that end, using laboratory stream channels, this study investigated whether a non-target species, the Rainbow Trout (Oncorhynchus mykiss Walbaum, 1792), would change their distribution in response to damage-released P. marinus alarm cues, potential mammalian predator cues, and damage-released conspecific cues. In groups of 10 individuals per replicate, with 10 replicates per stimulus type, subjects were exposed to one of the following treatment types: deionized water (control), P. marinus extract (heterospecific alarm cue), O. mykiss extract (conspecific alarm cue), 2-phenylethylamine hydrochloride (potential predator cue), and human saliva (potential predator cue). None of the stimuli induced a significant avoidance response during the stimulus observation period as compared to the control. These findings provide preliminary support for the species-specific nature of these proposed alternative P. marinus control measures.

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)0.002
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 score0.034
Threshold uncertainty score0.481

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0000.000

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 head0.004
GPT teacher head0.217
Teacher spread0.213 · 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

Citations0
Published2021
Admission routes2
Has abstractyes

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