Detection and Prediction of Toxic Aluminum Concentrations in High-Priority Salmon Rivers in Nova Scotia
Bibliographic record
Abstract
Abstract Elevated concentrations of toxic cationic aluminum (Ali) are symptomatic of terrestrial and freshwater acidification and are particularly toxic to salmonid fish species such as Atlantic salmon (Salmo salar). Speciated metal samples are rarely included in standard water monitoring protocols, and therefore the processes affecting Ali dynamics in freshwater remain poorly understood. Previous analysis of Ali concentrations in Nova Scotia (Canada) rivers found that the majority of study rivers had concentrations exceeding the threshold for aquatic health, but a wide-scale survey of Ali in Nova Scotia has not taken place since 2006 (Dennis, I. F., & Clair, T. A., 2012, Canadian Journal of Fisheries and Aquatic Sciences, 69(7), 1174–1183). The observed levels of dissolved aluminum in Atlantic salmon (Salmo salar) rivers of Atlantic Canada have potential serious and harmful effects for aquatic populations. We present the findings of the first large-scale assessment of the Ali status of Nova Scotia rivers in 17 years; we measured Ali concentrations and other water chemistry parameters at 150 sites throughout the Southern Uplands region of Nova Scotia from 2015 to 2022. We found that Ali concentrations exceeded toxic thresholds at least once during the study period at 80% of the study sites and that Ali concentrations increased during the study period at all four large-sample study sites. Modeling of relationships between Ali concentrations and other water chemistry parameters showed that the most important predictors of Ali are concentrations of the dissolved fractions of Al, iron, titanium, and calcium, as well as dissolved organic carbon and fluoride. We developed a fully Bayesian linear mixed model to predict Ali concentrations from a test data set within 15 μg/L. This model may be a valuable tool to predict Ali concentrations in rivers and to prioritize areas where Ali should be monitored. Environ Toxicol Chem 2024;43:2545–2556. © 2024 The Author(s). Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of 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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".