Use of Fish Biomarkers to Assess the Contaminant Exposure and Effects in Lake Erie Tributaries
Bibliographic record
Abstract
Contamination by humans in the Great Lakes ecosystem limits its recreational uses and threatens its commercial and sport fisheries (http://www.great-lakes.net/).In 1983, the International Joint Commission (an organization established by the Boundary Waters Treaty of 1909 between the United States and Canada) reported that 900 chemicals and heavy metals that are potentially dangerous to human health and biota had been identified in Great Lakes.The Commission designated 43 Areas of Concern (AOCs) along the shoreline of Great Lakes (IJC 1987a).Among the 43 AOCs, eight are located on Lake Eire (Figure 1.1).They are Lake Erie tributaries including the River Raisin in Michigan, Maumee River, Black River, Cuyahoga River and Ashtabula River in Ohio, Presque Isle Bay in Pennsylvania, Buffalo River in New York, and Wheatley Harbor in Ontario, Canada.Four AOCs, the St. Clair River, Detroit River, Clinton River and Rouge River are located on the connecting channel between Lake Erie and Lake Huron in Michigan and one AOC, the Niagara River, is located on the connecting channel between Lake Erie and Lake Ontario in New York.Surveys focused on the health of benthic fish, in particular brown bullheads (Ameiurus nebulosus), and concentrations of contaminants in sediments of the Lake Erie AOCs started in the early 1980s.The very first studies included those conducted at the Black River (Baumann et al. 1982) and the Niagara River (Black 1983).Both studies found elevated prevalence of tumors in fish from the rivers contaminated with PAHs in comparison with the relatively clean sites.Additional studies followed on the Black, Cuyahoga, Ashtabula, Detroit, Niagara and Buffalo Rivers, and Presque Isle Bay (Rice et al. 1986; Hickey et al.
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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 | 0.000 | 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) | 0.000 | 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".