Microtrolling: an Economical Method to Nonlethally Sample and Tag Juvenile Pacific Salmon at Sea
Bibliographic record
Abstract
Abstract Mortality of juvenile Pacific salmon Oncorhynchus spp. in their first marine year is hypothesized to be a primary driver of variable recruitment in a changing ocean. Much contemporary research focuses on diet, distribution, growth, and survival during this period; however, existing methods of capturing juvenile salmon at sea are expensive and may be limited by topography and tidal currents. We assessed the feasibility of using a small vessel and modified recreational fishing gear (microtrolling) to nonlethally capture, sample, and tag juvenile Chinook Salmon O. tshawytscha during their first marine summer. Sampling was conducted in the Strait of Georgia, British Columbia, from August to October 2014. We captured 168 Chinook Salmon, 13 Coho Salmon O. kisutch, and 1 Chum Salmon O. keta in 72 h of active fishing; Chinook Salmon CPUE was greatest between 6 and 19 m and increased late in the afternoon and on the flood tide. To assess short‐term mortality related to capture, PIT tagging, and sampling, we maintained 66 microtroll‐captured Chinook Salmon (41 of which were PIT‐tagged) overnight in a net pen; only one mortality and one incidence of tag loss were observed. Microtrolling proved effective for systematically sampling juvenile Chinook Salmon across depths and habitats. Unlike alternative methods of sampling juvenile Pacific salmon (trawling and purse seining), the utility of microtrolling is likely limited to studies of Chinook Salmon and possibly Coho Salmon. The low cost of this method has potential to facilitate participation of frequently excluded stakeholders, including First Nations and community groups, in marine research on juvenile Pacific salmon. Received June 10, 2016; accepted November 1, 2016 Published online February 21, 2017
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".