Lake Superior Has Lost over 90% of Its Pesticide HCH Load since 1986
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The time trend of α- and γ-hexachlorocyclohexane (HCH) isomers in Lake Superior water was followed from 1986 to 2016, the longest record for any persistent organic pollutant (POP) in Great Lakes water. Dissipation of α-HCH and γ-HCHs was first order, with halving times ( t 1/2 ) of 5.7 and 8.5 y, respectively. Loss rates were not significantly different starting a decade later (1996–2016). Concentrations of β-HCH were followed from 1996–2016 and dissipated more slowly ( t 1/2 = 16 y). In 1986, the lake contained an estimated 98.8 tonnes of α-HCH and 13.2 tonnes of γ-HCH; by 2016, only 2.7% and 7.9% of 1986 quantities remained. Halving times of both isomers in water were longer than those reported in air, and for γ-HCH, they were longer in water than those reported in lake trout. Microbial degradation was evident by enantioselective depletion of (+)α-HCH, which increased from 1996 to 2011. Volatilization was the main removal process for both isomers, followed by degradation (hydrolytic and microbial) and outflow through the St. Mary’s River. Sedimentation was minor. Major uncertainties in quantifying removal processes were in the two-film model for predicting volatilization and in microbial degradation rates. The study highlights the value of long-term monitoring of chemicals in water to interpreting removal processes and trends in biota.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".