Evaluation of the effects of catch‐and‐release angling on the Atlantic salmon (<i>Salmo salar</i>) of the Ponoi River, Kola Peninsula, Russian Federation
Bibliographic record
Abstract
Abstract – We studied the effects of catch‐and‐release fishing upon the Ponoi River's Atlantic salmon populations. The Ponoi River is located on the Kola Peninsula of the Russian Federation, and has recently been developed for sports fishing. Angler exploitation rates are estimated to range from 10.4% to 19% of the river's salmon, thus the possibility of significant levels of post‐release mortality is of concern. We radio‐tracked fish caught and released by anglers in 1995 and 1996. Despite our simple equipment and the large size of the river, we were able to relocate most fish. These fish had high rates of survival, and anglers recaptured about 11% of them per year a second time. This is very similar to the recapture rates observed for Floy‐tagged fish released in an angler‐based mark‐recapture assessment. We also held 62 angled fish for 24 hours in a live cage to evaluate rates of delayed mortality. Only one of the 62 fish died, and it was heavily scarred with gillnet marks. Most fish that are fatally stressed by angling die within 24 h (e.g., Booth et al. 1995). In 1996, up to 10% of our Floy‐tagged fish were angled and released twice, and about 0.5% were angled and released three times. No significant biases were detected in the post‐angling movement patterns of these fish. The multiple captures and lack of movement bias suggest that fish behavior was little altered by the angling experience. Nine fish Floy tagged prior to spawning have been recovered as typical emaciated kelts. Three were killed, and a post mortem exam showed all had spawned. Parr numbers at all monitored sites have been steadily increasing since the advent of catch‐and‐release fishing. By contrast, parr growth rates are generally unchanged or significantly better. NOTE
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".