Relative abundance of farmed Atlantic salmon, Salmo salar L. 1758, juveniles in wild samples from three southwestern New Brunswick rivers
Bibliographic record
Abstract
A procedure of classification using a discriminant function analysis was developed to determine the farmed or native natal origin of Atlantic salmon juveniles in the Magaguadavic River, New Brunswick. Farmed juveniles enter this river as escapees from three commercial aquaculture hatcheries. The procedure evaluated measured scale characteristics from the first year of growth, of farmed and native juveniles of known origin, for their power as predictors of derivation. Eight scale characteristics proved to be significant predictors of origin. In a jackknife cross-validation, the function developed from the characteristics proved to be 90.3% accurate in predicting the origin of juvenile Atlantic salmon in the Magaguadavic River. The procedure was then applied to unknown origin juveniles sampled from the Magaguadavic, Waweig and Digdequash rivers in New Brunswick. All of these rivers support hatcheries. Juvenile salmon sampled in the Magaguadavic River in 1996, 1997 and 1998, were determined to be 34%, 63% and 42% of farmed origin, respectively. During 1998, 9% of the juveniles from the Digdequash River were of farmed origin, and 42% of the juveniles in the Waweig River were of farmed origin. The study indicated that substantial numbers of farmed juveniles escaped from hatcheries and occupied juvenile salmon habitat in all three rivers.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".