MétaCan
Menu
Back to cohort
Record W2268040935 · doi:10.1111/fme.12135

Rapid assessment of the physiological impacts caused by catch‐and‐release angling on blue‐finned mahseer (<i>Tor</i> sp.) of the Cauvery River, India

2016· article· en· W2268040935 on OpenAlexafffund
Shannon D. Bower, Andy J. Danylchuk, Rajeev Raghavan, Sascha Clark-Danylchuk, Adrian C. Pinder, Steven J. Cooke

Bibliographic record

VenueFisheries Management and Ecology · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsCarleton University
FundersNational Institute of Food and AgricultureNatural Sciences and Engineering Research Council of CanadaMohammed bin Zayed Species Conservation FundInnovative Research Group Project of the National Natural Science Foundation of ChinaU.S. Department of Agriculture
KeywordsFishingDried fishBiologyFisheryVeterinary medicineFish <Actinopterygii>ToxicologyZoologyMedicine

Abstract

fetched live from OpenAlex

Abstract Forty‐nine blue‐finned mahseer ( Tor sp.; mean total length 458 ± 20 mm) were angled using a range of bait/lure types, angling and air exposure times in water that averaged 27 ± 2 °C over the course of the assessment. No cases of mortality were observed, and rates of moderate and major injury were low, with 91% of mahseer hooked in the mouth. More extreme physiological disturbances (i.e. blood lactate, glucose, pH ) in mahseer were associated with longer angling times. Sixteen fish (33%) exhibited at least one form of reflex impairment. Moreover, longer air exposures and angling times resulted in significant likelihood of reflex impairment. Findings suggest that blue‐finned mahseer are robust to catch‐and‐release, but that anglers should avoid unnecessarily long fight times and minimise air exposure to decrease the likelihood of sub‐lethal effects that could contribute to post‐release mortality.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score0.664

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.009
GPT teacher head0.197
Teacher spread0.189 · 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

Citations19
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueFisheries Management and EcologySame topicFish Ecology and Management StudiesFrench-language works237,207