A dynamic mass budget for toxaphene in North America
Bibliographic record
Abstract
A continental-scale dynamic mass budget for toxaphene in North America is presented, based on available information on physicochemical properties, usage patterns, and reported environmental concentrations and using the Berkeley-Trent North American mass balance contaminant fate model (BETR North America). The model describes contaminant fate in 24 ecological regions of North America, including advective transport between regions in the atmosphere, freshwater, and near-shore coastal water. The dynamic mass budget accounts for environmental partitioning, transport, and degradation of the estimated 534 million kg of toxaphene that were used in North America as an insecticide and piscicide between 1945 and 2000. Satisfactory agreement exists between model results and current and historically reported concentrations of toxaphene in air, water, soil, and sediments throughout North America. An estimated 15 million kg of toxaphene are believed to remain in active circulation in the North American environment in the year 2000, with the majority in soils in the southern United States and Mexico, where historic usage was highest. Approximately 70% of total toxaphene deposition from the atmosphere to the Great Lakes is attributed to sources outside the Great Lakes Basin, and an estimated total of 3.9 million kg of toxaphene have been transported to this region from other parts of the continent. The toxaphene mass budget presented here is believed to be the first reported continental-scale multimedia mass budget for any contaminant.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".