Bayesian hierarchical modeling of seven years of inter-stage survival rates of wild Atlantic salmon smolt and post-smolt from three rivers of eastern Canada
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author. Mortality of Atlantic salmon post-smolt has been assumed to be highest in the first few months of migration at sea due to their small body size and the stress associated with acclimation to the marine environment. We report on research undertaken to estimate the location and timing of mortality of smolt during the first 2 months at sea. More than 1,700 wild Atlantic salmon smolt from three rivers of the Gulf of St. Lawrence (Canada) were acoustically tagged and released from 2003 to 2013. Acoustic arrays were first installed and monitored at the head of tide of each river, and at the exit of these rivers to the Gulf of St. Lawrence. In 2007 an array became fully operational in the Strait of Belle Isle (SoBI), the Gulf of St. Lawrence exit leading to the Labrador Sea, about 800 km from the point of smolt release. A Bayesian state-space model variant of the Cormac-Jolly-Seber model was used to disentangle the imperfect detection of tagged smolt on the acoustic arrays from apparent survival during their out migration. The model reduced uncertainty in expected values of the annual and river specific detection probabilities at the head of tide and bay exit arrays, however, it was not possible to independently resolve the detection probabilities at the SoBI array and the probability of survival through the Gulf of St. Lawrence. This telemetry research provides useful guidance in the design of such experiments and the treatment of data.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".