Distribution of haemic neoplasia of soft‐shelled clams in Prince Edward Island: an examination of anthropogenic factors and effects of experimental fungicide exposure
Bibliographic record
Abstract
Haemic neoplasia was first considered a disease of concern for soft-shell clams in Prince Edward Island (PEI) when it was diagnosed as the cause of mass mortalities in 1999. The aetiology of the disease remains elusive, but has been associated with environmental degradation. In this study, a 2-year (2001-2002) geographic and seasonal survey was conducted for haemic neoplasia, using histology, in soft-shell clams from PEI. In addition, using geographic information system, the association between anthropogenic factors in the watersheds at sites affected by haemic neoplasia and the prevalence of the disease was investigated. Finally, histopathological changes were assessed in soft-shell clams experimentally exposed to four concentrations of chlorothalonil for 27 days. Haemic neoplasia could not be induced at any concentration of chlorothalonil. Clams exposed to a concentration of 1000 μg L(-1) of the fungicide, however, exhibited an LC50 of 17 days. Although this information provides additional toxicity information (LC50) for soft-shell clams, further experiments are required to assess longer term exposure to the fungicide. The highest prevalences of haemic neoplasia in PEI were found in North River and Miscouche (28.3-50.9% and 33.0-77.8%, respectively). No clear seasonal patterns were found. There was a correlation between haemic neoplasia prevalence and watersheds with a high percentage of potato acreage and forest coverage (P = 0.026 and P = 0.045, respectively), suggesting a link between anthropogenic activity and the prevalence of the disease.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".