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Record W1979224131 · doi:10.1080/02755947.2012.675962

Comparison of Catch Efficiencies between Black and Galvanized Minnow Traps

2012· article· en· W1979224131 on OpenAlexafffund
Yves Paradis, Angélique Dupuch, Pierre Magnan

Bibliographic record

VenueNorth American Journal of Fisheries Management · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversité LavalNatural Sciences and Engineering Research Council of CanadaMinistère des Ressources naturelles et des ForêtsUniversité du Québec à Trois-RivièresMinistère des Ressources naturelles et des Forêts (Québec)
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsMinnowGalvanizationFisheryEnvironmental scienceSuckerLepomisPhoxinusFish <Actinopterygii>BiologyZoologyChemistry

Abstract

fetched live from OpenAlex

Abstract Minnow traps are widely used in aquatic ecology for the quantitative sampling of small-bodied fish. We used paired sampling stations to compare the catch efficiency of minnow traps constructed of galvanized steel with that of traps constructed of steel mesh covered with a black vinyl coating in field and laboratory conditions. Except for northern redbelly dace Phoxinus eos in field sampling, the catch efficiency of the galvanized minnow traps was higher than that of the black traps. Catches of pumpkinseed Lepomis gibbosus, creek chub Semotilus atromaculatus, and white sucker Catostomus commersonii were up to five times higher in the galvanized minnow traps than in the black ones. For the quantitative estimation of small-bodied fish, we recommend using only galvanized minnow traps to maximize catch efficiency. If data are intended for quantitative analyses, mixing minnow trap models in a field survey could lead to inappropriate catch comparisons. Received October 31, 2011; accepted January 23, 2012

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.016
GPT teacher head0.250
Teacher spread0.233 · 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 source (direct Gemma or distilled Codex), 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

Citations7
Published2012
Admission routes2
Has abstractyes

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