The Influence of Water Temperature on Time to Surface for Adult Sockeye Salmon Carcasses and the Limitations in Estimating Salmon Carcasses in the Fraser River, British Columbia
Bibliographic record
Abstract
Abstract We determined the postmortem specific gravity and examined the relationship between water temperature and the time to surface for carcasses of adult sockeye salmon Oncorhynchus nerka. These results, in conjunction with other biophysical factors, were used to explain the difficulties associated with the detection of the carcasses of sockeye salmon that die during upstream migration in the Fraser River. Large negative differences have occurred between the sockeye salmon estimates at the river entry and spawning escapement (>0.5 million in 8 of the past 15 years), but relatively few carcasses are observed. Without confirmation of mortality, managers are left with the dilemma of how to partition these differences among lower river escapement, spawning ground escapement, catch, en route mortality, and unreported catch. We investigated the reasons for the paucity of carcass observations by experimentally testing the influence of water temperature on carcass buoyancy and by examining hydrological factors that may affect carcass visibility. Because the Fraser River is turbid (Secchi depth, <40 cm), only carcasses on the surface can be observed. Initially, all fish had specific gravity estimates of greater than 1 (mean ± SD, 1.057 ± 0.010) and were negatively buoyant when placed in tanks at different water temperatures. The time for a sockeye salmon carcass to surface was inversely related to water temperature and ranged from 1–3 d at 20°C to 12–18 d at 6°C. Annual counts of dead fish floating on the surface were positively correlated with river water temperature. Fisheries managers should not always expect to see large numbers of dead salmon in years of high estimated en route losses, given the factors that influence the visibility of salmon carcasses in a large river system. We recommend alternative methods for achieving more accurate and precise estimates of en route mortalities.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".