Lake Ontario: the predominant source of triazine herbicides in the St. Lawrence River
Bibliographic record
Abstract
To estimate triazine herbicide concentrations and sources in the St. Lawrence River, water samples were collected at its two major inlets (from the Great Lakes, Cornwall station, and from the Ottawa River, Carillon station) and at the outlet (Quebec City station) of the fluvial section. Sampling was carried out over an 18-month period between 1995 and 1996. Triazines were detected only in the dissolved phase at concentrations ranging from 2 to 91, from <0.4 to 15, and from <0.4 to 13 ng·L-1 for atrazine, cyanazine, and simazine, respectively. Dilution models show that, despite the presence of sporadically high concentrations of herbicides in St. Lawrence tributaries during periods of their application, loading from the tributaries is minor. Mass balance calculations show that Lake Ontario is clearly the main source of triazines (~90%) to the St. Lawrence River. During the 1995-1996 hydrological year, Lake Ontario contributed 15.1 × 103 of the 16.6 × 103 kg of atrazine outflowing the St. Lawrence River to the estuary. The difference (1.5 × 103 kg·year-1) can be attributed to tributaries in Quebec, which represent 0.75% of the amount of atrazine spread on farmlands. There is no evidence of the degradation of triazine compounds during their transit time in the river.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 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 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".