Current Use Pesticides that drain into Canadian tributaries: A potential threat to Whale habitats
Bibliographic record
Abstract
Elevated contaminant concentrations in odontocete cetaceans within Canadian waters has been well documented. The Endangered transboundary Southern Resident Killer Whales (SRKW, Orcinus orca) and St Lawrence Estuary Beluga Whales (SLE beluga, Delphinapterus leucas) face significant threats from high levels of contaminants. The Recovery Strategy for the SLE beluga, SRKWs, as well as the Threatened Northern Resident Killer Whales (NRKW) lists contaminants as a key threat to these whale populations and identifies urban and agricultural runoff and stormwater as pollutant sources. This runoff exposes the whales and their priority prey to a mixture of environmental contaminants, including current use pesticides. Our main objective was to compare levels, loads, and yields of seven current use pesticides (atrazine, chlorpyrifos, diazinon, glyphosate, malathion, permethrin, and simazine) in tributaries within urban and agricultural areas that could impact the habitat of the whales and their prey. These include major Canadian metropolitan areas: 1) the Great Lakes Region (southern Ontario), 2) the Fraser River Basin (British Columbia), and 3) St. Lawrence Region (southern Quebec). The Great Lakes work focuses on tributaries that drain into Lake Ontario, which discharges into the St. Lawrence River its Atlantic estuary. Lake Ontario is the major contributor of pesticide pollution to the St. Lawrence River. Availability and quality of Chinook salmon has been identified as the priority threat to SRKWs. Fraser River Chinook make up a large percentage of the SRKW diet and may be impacted by current use pesticide discharges in the area. Preliminary results show that glyphosate had the highest yields across all sites followed by simazine (Fraser River Basin) and atrazine (Great Lakes and St. Lawrence). Exceedances of environmental water quality guidelines were observed for atrazine, chlorpyrifos, and diazinon. Pesticide hot-spots and exceedances will be discussed in the context of whale recovery.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".