Residual DDT distribution in the soils and sediments of Point Pelee National Park: Implications and Tools for Remediation
Bibliographic record
Abstract
Point Pelee National Park (PPNP), located in Leamington, ON, is heavily contaminated with the pesticide dichlorodiphenyltrichloroethane (DDT) that was liberally used for mosquito and pest control in the park from the 1940s until the 1960s. This study was designed to update and enhance information that will advise PPNP personnel on remediation strategies. Building on previous research, a comprehensive soil and sediment sampling, and analytical program was carried out over several years and was completed in 2014. In total, 140 soil, nine sediment, and four water samples were analyzed by gas chromatography/electron capture detection. Dichlorodiphenyltrichloroethane contamination boundaries were defined, and they were determined that this contaminant occurs predominantly in three “hot spot” areas with total DDT levels exceeding 130 000 ng g−1, which is 19 000% higher than federal guidelines. This information was mapped into an interactive Google Earth platform. Dichlorodiphenyltrichloroethane isomer analysis compared groupings of samples and determined that soil hot spot areas have half-lives ranging from 27 to 40 yr. It was determined that the highest concentrations of DDT (not including DDT’s derivatives) could remain above federal guidelines for a further 220–342 yr. Overall this study improved delineation of DDT hot spots and narrowed the half-life ranges of DDT and its metabolites in PPNP.
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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 | 0.001 | 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) | 0.001 | 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".