Discrimination of trait-based characteristics by trace element bioaccumulation in riverine fishes
Bibliographic record
Abstract
Relations between tissue trace element concentrations and species traits were examined for 45 fish species to determine the extent to which trait-based characteristics accounted for relative differences among species in trace element bioaccumulation. Percentages of fish species correctly classified by discriminant analysis according to traits predicted by tissue trace element concentrations ranged from 72% to 87%. Tissue concentrations of copper, mercury, selenium, and zinc appeared to have the greatest overall influence on differentiating species according to trait characteristics. Discrimination of trait characteristics did not appear to be strongly influenced by local sources of trace elements in the streambed sediment. Bioaccumulation was greatest for those species classified as primarily detritivores, having relatively large adult body size, considered nonmigratory with respect to reproductive strategy, occurring mostly in large or variable size streams and rivers, preferring depositional areas within the stream channel, and preferring benthic rather than open-water habitats. Our findings provide evidence of the strong relationship between bioaccumulation of environmental trace elements and trait-based factors that influence contaminant exposure.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".