Food chain model based on field data to predict westslope cutthroat trout (<i>Oncorhynchus clarkii lewisi</i>) ovary selenium concentrations from water selenium concentrations in the Elk Valley, British Columbia
Bibliographic record
Abstract
Previous studies conducted in the Elk River watershed showed that selenium concentrations are higher in aquatic biota in lentic compared to lotic habitats of the system having similar water selenium concentrations. Studies have also shown that water selenium concentrations have increased over time (~10% per year) and recent annual average concentrations have ranged up to 0.044 mg/L in areas downstream from mine discharges. For the present study, trophic transfer of selenium was characterized in lotic versus lentic habitats using concentrations measured in field-collected samples and assuming a three-step food chain of water to the base of the food web (biofilm), to benthic invertebrates, and then to westslope cutthroat trout (WCT) ovaries. Food chain models were developed for each habitat type (lotic and lentic) by combining linear regression equations for the three transfer relationships, allowing for prediction of fish ovary concentrations from water concentrations. Greater accumulation of selenium in lentic areas was mostly attributable to greater uptake at the base of the food chain compared to lotic areas. Enrichment/trophic transfer factors for selenium at all levels of the lotic and lentic food chains decreased and then became near constant as exposure concentrations increased. The lotic model predicted little increase in WCT ovary selenium concentrations over an eightfold increase in water concentrations (~0.005-0.040 mg/L), accounting for the lack of observed increase in within-area fish tissue concentrations over time despite increasing trends in water concentrations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".