Survey of mercury in boreal chorus frog (<i>Pseudacris maculata</i>) and wood frog (<i>Rana sylvatica</i>) tadpoles from wetland ponds in the Prairie Pothole Region of Canada
Bibliographic record
Abstract
Tadpoles are important prey items for many aquatic organisms and often represent the largest vertebrate biomass in many fishless wetland ecosystems. Neurotoxic mercury (Hg) can, at elevated levels, decrease growth, lower survival, and cause developmental instability in amphibians. We compared total Hg (THg) body burden and concentration in boreal chorus frog ( Pseudacris maculata) and wood frog ( Rana sylvatica) tadpoles. Overall, body burden and concentration were lower in boreal chorus frog tadpoles than wood frog tadpoles, as expected, because boreal chorus frog tadpoles consume at lower trophic levels. The variables species, stage, and mass explained 21% of total variation for body burden in our models but had negligible predictive ability for THg concentration. The vast majority of the remaining variation in both body burden and THg concentration was attributable to differences among ponds; tadpoles from ponds in three areas had considerably higher THg body burden and concentration. The pond-to-pond differences were not related to any water chemistry or physical parameter measured, and we assumed that differences in wetland geomorphology likely played an important role in determining Hg levels in tadpoles. This is the first report of Hg in frog tadpoles in the Prairie Pothole Region of North America.
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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 | 0.000 |
| 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 | 0.000 | 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".