Factors affecting selenium bioconcentration at the base of aquatic food webs
Bibliographic record
Abstract
Selenium is a naturally occurring element and an essential micronutrient for many organisms; however, at high concentrations it can become toxic. Currently, the mechanisms underlying selenium accumulation remain unclear, resulting in uncertainty in the prediction of selenium transfer from water to primary producers at the base of the food web – a process referred to as enrichment. This study assesses how varying concentrations of selenium and sulphate in water affect enrichment. Using reported concentrations of selenium, in water and periphyton collected from three mining regions in British Columbia, Canada, we show that enrichment is inversely related to exposure concentration. The effect of sulphate on enrichment was explored by comparing the fit of multivariate regression models (with and without sulphate) with Akaike’s Information Criterion (AIC). Models without sulphate were significantly better at predicting enrichment than models with sulphate (∆AICc = 2.29); however, conclusions were limited due to collinearity between selenium and sulphate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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 teacher head, 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".