DISTRIBUTION AND OCCURRENCE OF ATRAZINE, DEETHYLATRAZINE, AND AMETRYNE RESIDUES IN GROUNDWATER OF THE TROPICAL ISLAND BARBADOS
Bibliographic record
Abstract
The triazine herbicides atrazine (AT) and ametryne (AMET) are used extensively in Barbados to control weeds in sugar cane fields. These pesticides were monitored in groundwater (the primary source of potable water on the island) for the dry seasons of March–June, 1991 and January–May, 1992 and the wet season, July–December, 1992. Residues of either AT or AMET were detected in all 277 groundwater samples collected from 23 wells in three major groundwater catchments underlying heavily cultivated sugar cane fields. The concentration of AT and the metabolite deethylatrazine (DEAT) were in the range 0.11–2.61 μg/l with the highest occurrence in the most heavily cultivated sugar cane area, the Hampton catchment. These measured levels of atrazine in the groundwater, represent a relatively small fraction that may have leached from the annual application, and was estimated to be 0.12, 0.17 and 0.19% of the total amount applied to the Belle, Hampton and Western catchments respectively. These levels, however, are in general higher than those reported for related studies and lower application rates of atrazine in sugar cane plantations should be considered to help preserve the quality of potable water on the island.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".