Relationship among mercury concentration, growth rate, and condition of northern pike: A tautology resolved?
Bibliographic record
Abstract
Methylmercury is a bioaccumulative contaminant that biomagnifies in aquatic food webs and adversely affects the health of freshwater fish. Previous studies have documented an inverse relationship between fish condition and concentration of mercury in fish. However, this relationship may be a result of slow-growing fish accumulating large amounts of methylmercury rather than the effects of methylmercury on fish condition and growth. An evaluation was conducted of the relationship among fish condition, growth, and mercury concentration in northern pike Esox lucius from 26 lakes in the western region of the Laurentian Great Lakes (USA-Canada). The relative weight (an index of fish condition) of northern pike was inversely related to mercury concentration in the axial muscle. The concentration of mercury in standard-size northern pike increased with fish age and suggested that fast-growing fish accumulated less mercury than slow-growing fish. However, there was no relationship between the mean relative weight of northern pike in each population and mean age or mercury concentration of standard-size northern pike. These results suggest that the relationship between mercury and fish condition is not because of the effects of mercury on rate of growth, but rather because slow-growing fish bioaccumulate greater concentrations of mercury than fast-growing fish of the same length. Environ Toxicol Chem 2016;35:2910-2915. © 2016 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".