Tracking\nOverwintering Areas of Fish-Eating Birds to Identify Mercury Exposure
Bibliographic record
Abstract
Migration\npatterns are believed to greatly influence concentrations\nof contaminants in birds due to accumulation in spatially and temporally\ndistinct ecosystems. Two species of fish-eating birds, the Double-crested\nCormorant (<i>Phalacrocorax auritus</i>) and the Caspian\nTern (<i>Hydroprogne caspia</i>) breeding in Lake Ontario\nwere chosen to measure the impact of overwintering location on mercury\nconcentrations ([Hg]). We characterized (1) overwintering areas using\nstable isotopes of hydrogen (δ<sup>2</sup>H) and band recoveries,\nand (2) overwintering habitats by combining information from stable\nisotopes of sulfur (δ<sup>34</sup>S), carbon (δ<sup>13</sup>C), nitrogen (δ<sup>15</sup>N), and δ<sup>2</sup>H in\nfeathers grown during the winter. Overall, overwintering location\nhad a significant effect on [Hg]. Both species showed high [Hg] in <sup>13</sup>C-rich habitats. <i>In situ</i> production of Hg\n(e.g., through sulfate reducing bacteria in sediments) and allochthonous\nimport could explain high [Hg] in birds visiting <sup>13</sup>C-rich\nhabitats. Higher [Hg] were found in birds with high δ<sup>2</sup>H, suggesting that Hg is more bioavailable in southern overwintering\nlocations. Hotspot maps informed that higher [Hg] in birds were found\nat the limit of their southeastern overwintering range. Mercury concentrations\nin winter feathers were positively related to predicted spatial pattern\nof [Hg] in fish using the National Descriptive Model of Mercury in\nFish (NDMMF) based on bird spatial assignment (using δ<sup>2</sup>H). This study indicates that the overwintering location greatly\ninfluences [Hg].
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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 | 0.000 |
| 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) | 0.031 | 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".