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Record W2065173733 · doi:10.1080/02755947.2011.591221

Verifying Identification of Salmon and Trout by Boat Anglers in Lake Ontario

2011· article· en· W2065173733 on OpenAlexafffundabout
James N. Bowlby, Paul J. Savoie

Bibliographic record

VenueNorth American Journal of Fisheries Management · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsFisheries and Oceans CanadaMinistry of Natural Resources and Forestry
FundersMinistry of Natural Resources
KeywordsChinook windFisherySalmoOncorhynchusTroutRainbow troutSalvelinusFishingFish <Actinopterygii>SalmonidaeBrown troutGeographyBiology

Abstract

fetched live from OpenAlex

Abstract We estimated how well anglers with varying levels of fishing experience identified six salmonid species during creel surveys of the boat angler fishery in Canadian waters of Lake Ontario (1995 and 1996). Anglers were asked to identify the species of their harvested salmon and trout. Angler identifications were compared with identifications made by creel survey technicians. In total, 583 noncharter anglers and 92 charter boat captains identified 1,098 and 271 fish, respectively. Chinook salmon Oncorhynchus tshawytscha, rainbow trout O. mykiss, and lake trout Salvelinus namaycush dominated the observations. The greatest accuracy in identification by noncharter anglers was for lake trout (96%), followed by Chinook salmon (93%), rainbow trout (88%), brown trout Salmo trutta (85%), Atlantic salmon Salmo salar (67%), and coho salmon O. kisutch (60%). Identifications by charter captains were closer to identifications made by the creel survey technicians: accuracy for coho salmon and rainbow trout was 100%, followed by Chinook salmon (97%), lake trout (96%), and brown trout (86%). Noncharter anglers with over 8 years of salmonid angling experience on Lake Ontario identified coho salmon more accurately (79%) than anglers with fewer years of experience (18%). Noncharter anglers’ fishing experience had no relationship with identification accuracy for species other than coho salmon; angling experience among charter captains had no effect on identification accuracy for any species. Among Chinook salmon, accurately identified individuals were significantly larger than misidentified fish. Size probably plays a role in identification of Chinook salmon as they are the largest salmonids in the fishery. Accurate identification of salmonids in Lake Ontario allows Ontario fisheries biologists to use catch rates with confidence, and these results may be applicable on a widespread basis throughout the Great Lakes. Received April 7, 2010; accepted January 1, 2011

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)0.003
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 score0.137
Threshold uncertainty score0.276

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.184
Teacher spread0.174 · 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

Citations6
Published2011
Admission routes3
Has abstractyes

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