Radial Dilution Model for the Distribution of Toxaphene in the United States and Canada on the Basis of Measured Concentrations in Tree Bark
Bibliographic record
Abstract
Toxaphene was a highly chlorinated pesticide that was used in the United States from the 1950s until its restriction and ban in the 1980s. It was primarily used on cotton in the southern United States, and perhaps as a result, toxaphene is found in high concentrations in the southern United States. Toxaphene has also been detected at remote locations, such as in the high Canadian Arctic. However, these and other studies focused on selected regions of the United States and Canada, which only allowed for a limited interpretation of these data. We report here the concentrations of toxaphene measured in 46 tree bark samples collected in the United States and Canada. Concentrations were found to be greatest in the Mississippi River Valley along the borders of southern Missouri and Arkansas. We created a simple model based on radial dilution from a central location in the south central United States to describe toxaphene concentrations in tree bark and in air throughout the United States and Canada. The toxaphene concentrations were successfully described by this inverse square distance model. A bark-atmosphere partition coefficient for toxaphene was also calculated that was similar to literature-derived octanol-air partition coefficients. High concentrations of toxaphene still exist in areas where it was heavily used but decline rapidly with distance.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".