ASSESSMENT OF DIOXIN CONTAMINATION IN THE ENVIRONMENT AND HUMAN POPULATION IN THE VICINITY OF DIOXIN HOTSPOT IN DA NANG AIRBASE, SOUTH VIETNAM
Bibliographic record
Abstract
The presence of dioxin in the environment in and around former US military sites in Viet Nam is a direct result of storage, use and spillage of herbicides by the US military and Army of the Republic of Viet Nam (ARYN) forces.Significant quantities of2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), a contaminant in Agent Orange, were detected in samples analyzed from the Da Nang Airbase in December 2006.Dioxin levels recorded in Da Nang soils, sediments, fish and human tissues exceeded all international standards and guidelines for these toxic chemicals.Similar situations can be expected at other major Ranch Hand sites in Viet Nam, especially Bien Hoa.Despite the fact that dioxins are known to be a significant environmental hazard, to date there have not been adequate measures taken to properly assess the extent and impact of contamination around known dioxin hot spots in Viet Nam.More than 40 years have elapsed since Agent Orange was introduced to the environment of Viet Nam, and the resultant chemical contamination continues to enter the environment and human population to this day.Mitigation measures must be implemented immediately to protect Vietnamese living in the vicinity of dioxin hot spots from further contamination and potential health impacts.
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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 | 0.000 | 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".