Management of bacterial canker of grapes, caused by Xanthomonas campestris pv. viticola in the São Francisco valley.
Bibliographic record
Abstract
The biomagnification of toxic methylmercury (MeHg) and selenium (Se) through aquatic food webs using nitrogen stable isotopes (δ<sup>15</sup>N) varies among ecosystems but underlying mechanisms are yet unexplained. Given the strong links between MeHg and thiol-containing amino acids and proteins containing selenocysteine, our hypothesis was that cysteine content is a better predictor of MeHg and Se transfer through lake food webs than δ<sup>15</sup>N. Food web samples were collected from six lakes in Kejimkujik National Park, Nova Scotia, Canada, and the regression slopes of log MeHg or Se versus protein-bound cysteine or bulk δ<sup>15</sup>N were compared. Across all six lakes, MeHg varied by a factor of 10 among taxa and was significantly and positively related to both cysteine (R<sup>2</sup> = 0.65-0.80, p < 0.001) and δ<sup>15</sup>N (R<sup>2</sup> = 0.88-0.94, p < 0.001), with no among-system differences in these slopes. In contrast, total Se concentrations varied by less than a factor of 2 among taxa in four lakes and were significantly related to cysteine in only two food webs (R<sup>2</sup> = 0.20 & 0.37, p = 0.014 & < 0.001); however, δ<sup>15</sup>N was not a predictor of Se in any lake (p = 0.052-0.777). Overall, these novel results indicate that cysteine content predicts MeHg, and sometimes Se, across trophic levels, providing a potential mechanism for among-system differences in their biomagnification.
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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) | 0.002 | 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 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".