Current-use pesticide transport to Costa Rica's high-altitude tropical cloud forest
Bibliographic record
Abstract
To gain insight into the atmospheric transport and deposition of organic contaminants in high-altitude forests in the humid tropics, pesticides were analyzed in air, water, and soil samples from Costa Rica. Passive samplers deployed across the country revealed annually averaged air concentrations of chlorothalonil, endosulfan, and pendimethalin that were higher in areas with intensive agricultural activities than in more remote areas. Atmospheric concentrations were particularly high in the intensively cultivated central valley. Only endosulfan and its degradation products were found in soils sampled along an altitudinal transect on the northern side of Volcano Turrialba, which is facing heavily cultivated coastal plains. Consistent with calculations of cold trapping in tropical mountains, concentrations of endosulfan sulfate increased with altitude. Pesticide levels in lake, creek, fog, and arboreal water samples from high-elevation cloud forests were generally below 10 ng · L(-1). Endosulfan sulfate was the most abundant pesticide in water, with concentrations ranging from 0.4 to 9.4 ng · L(-1). Its levels were highest in water sampled from bromeliads. Levels of total endosulfan in water are much lower than the reported median lethal concentration (LC50) value for acute toxicity of α-endosulfan to tadpoles. Although this suggests that the presence of pesticide might not have a direct impact on amphibian populations, the possibility of effects of chronic exposure to a mixture of substances cannot be excluded. Fog was relatively enriched in some of the analyzed pesticides, such as dacthal and chlorothalonil, and may constitute an important deposition pathway to high-altitude tropical cloud forest.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.005 | 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 teacher head, 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".