Organochlorine pesticides in soils of the horticultural belt of Bahía Blanca (Argentina)
Bibliographic record
Abstract
The land around Bahía Blanca, Argentina, has been farmed intensively for six decades. We report the concentrations of a number of organochlorine pesticides (DDT, dieldrin, endrin, heptachlor epoxide and lindane), and of DDT metabolites (DDD + DDE) in three different layers (0–5, 5–10 and 10–20 cm) of the soils in 44 fields of eight farms that have been devoted to horticulture for periods ranging from 15 to 60 yr. In spite of the horticultural use of these substances having been banned for the past 13–35 yr, high concentrations were found – up to nearly 12 mg kg-1 for DDT + DDD + DDE, 17 mg kg-1 for dieldrin, 4 mg kg-1 for endrin, 7 mg kg-1 for heptachlor epoxide and 0.8 mg kg-1 for lindane. The highest concentrations of DDT, dieldrin, endrin and heptachlor epoxide were found on the oldest farms, the highest levels of DDD + DDE on middle-aged farms (35–40 yr), and the hi ghest levels of lindane on 15–40-year-old farms that had rather lower soil pH than the older farms. Concentrations invariably decreased slightly with increasing depth, and for DDT, dieldrin and heptachlor epoxide they exhibited significant positive correlation with soil organic matter content. Principal components analysis confirmed the distinguishability of three groups of analytes: one comprising DDT, dieldrin, endrin and heptachlor epoxide associated with higher soil organic matter and clay contents; and two singletons, DDD + DDE associated with higher pH and lindane. We conclude that these pesticides have very limited mobility in these semiarid alkaline soils. Key words: Organochlorine, semiarid soils, horticulture, depth variation.
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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 | 0.001 | 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".