Diffuse geographic distribution of herbicides in northern prairie wetlands
Bibliographic record
Abstract
The concentrations of herbicides in water from wetlands on landscapes where herbicides are not used should be less than on farms with moderate (conventional farms) and intense (minimum-till farms) herbicide use. In general, this hypothesis was not supported for wetlands situated in the Boreal Plains Ecozone of central Saskatchewan, Canada. The overall detection frequency of 10 commonly used herbicides was not significantly different among wildlife habitat with no pesticide use (44.4%), farms with no pesticide use (51.6%), conventional farms (54.9%), and minimum-till farms (56.5%, chi 2 = 5.64, p = 0.13). The herbicides (4-chloro-2-methylphenoxy) acetic acid (MCPA), 2,4-dichlorophenoxyacetic acid (2,4-D), bromoxynil, dicamba, mecoprop, and diclorprop accounted for 87% of all detections. In general, detection frequencies and concentrations of individual herbicides were similar on all land-use types. For example, the mean concentration of 2,4-D in water on the four land types ranged from 0.12 +/- 0.104 to 0.26 +/- 0.465 microgram/L, and MCPA ranged from 0.08 +/- 0.078 to 0.19 +/- 0.166 microgram/L. However, in the year of application, mean concentrations of MCPA and bromoxynil, but not 2,4-D, were significantly higher by about twofold in wetlands situated in fields where these herbicides were applied compared with all other wetlands. We propose that many agricultural pesticides are rapidly lost to the atmosphere at the time of application by processes such as volatilization from soil and plant evapotranspiration. Then, the herbicides used throughout the region may be directly absorbed to the surface of wetlands from the atmosphere, or they become entrained in local convective clouds, and are redistributed by rainfall in a relatively homogenous mixture over the agricultural landscape. The low levels of individual herbicides we found in most of the wetland waters would not cause chronic effects to aquatic biota.
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 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".