Legacy DDT and its metabolites in Brook Trout from lakes within forested watersheds treated with aerial applications of insecticides
Bibliographic record
Abstract
To manage defoliation from insect outbreaks, about half of the forested land in New Brunswick, Canada, was treated with dichlorodiphenyltrichloroethane (DDT) between 1952 and 1968. Aerial applications of DDT have thus likely increased the risk of chronic effects in aquatic ecosystems from this legacy insecticide given its high persistence in soil and sediments and its bioaccumulation potential within the food web. We investigated DDT and its metabolites (total ΣDDTs = ∑ DDT + ∑ DDD + ∑ DDE) in Brook Trout (Salvelinus fontinalis) associated with geospatial data of historical applications to lake watersheds and sedimentary measures of DDT and its metabolites from five "impact" and two "reference" study lakes. Total ΣDDTs in recent lake sediments were significantly correlated with cumulative DDT applied aerially to the lake's watershed. Brook Trout muscle tissue showed total ΣDDTs that were significantly higher from impact lakes than reference lakes. On average, total ΣDDTs in Brook Trout from impact lakes exceeded ecological guidelines for consumers of aquatic biota by about ten times. Most legacy DDT in Brook Trout and lake sediments were the metabolites ΣDDE and ΣDDD, which suggests the importance of environmental conditions and transport of weathered sources of this organochlorine insecticide to biota. Stable isotopes from fish and common invertebrate prey also suggested that Brook Trout were at a similar trophic position among all study lakes and thus storage pools of legacy DDT likely explain contamination levels within biota. Our findings clearly demonstrate that chronic effects of historical DDT applications likely persist throughout aquatic environments in north-central New Brunswick.
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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".