Developing Triggers for Environmental Effects Monitoring Programs for Trout-Perch in the Lower Athabasca River (Canada)
Bibliographic record
Abstract
Baseline variations in trout-perch energy use (growth, gonad size) and energy storage (condition, liver size) were characterized between 2009 and 2015 in 8 reaches of the Athabasca River (AB, Canada), including 2 reaches upstream of the city of Fort McMurray (AB, Canada) and 6 reaches downstream of Fort McMurray among existing oil sands operations. Generalized linear models, used to account for background variation, indicated that fork length, gonad size, and liver size decreased, whereas body weight increased, in relation to river discharge, for both male and female trout-perch. Air temperature was positively correlated with liver size and negatively correlated with gonad size and body weight for females, but only positively correlated with gonad weight for males. These linear models explained approximately 20 to 25% of the variation in adjusted body size, and upward of 80% of the variation in adjusted body weight, liver weight, and gonad weight. Residuals from linear models were used to estimate normal ranges of variation for each of the fish population performance measures. Combined, the models and normal ranges can be used to assess subsequent monitoring data, providing potential triggers for follow-up monitoring activities. Environ Toxicol Chem 2019;38:0-0. Environ Toxicol Chem 2019;38:1890-1901. © 2019 SETAC.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".