Verifying Identification of Salmon and Trout by Boat Anglers in Lake Ontario
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".