Bibliographic record
Abstract
In the last century, agricultural intensification on the Canadian prairies has resulted in increased pesticide use with the potential to expose non-target organisms to pesticides as a result of non-point source pollution. In order to minimize risk and implement programs and regulations that promote sustainable agricultural practices, information on the types of pesticides being used and their subsequent fate in soils is essential. In this study, pesticide use trends were summarized and Herbicide Risk Indicators (HRIs) were calculated for the 1996-2006 growing seasons; a time period in which genetically modified herbicide-tolerant (GMHT) crops were commercially adopted. This study also quantified the influence of soil moisture, temperature, slope position, and soil depth within the plough layer on 2,4-D [2,4-(dichlorophenoxy) acetic acid] fate in soil obtained from a cultivated undulating field in Southern Manitoba. Annual pesticide use varied slightly over the 11-year period, but overall, there were no significant increasing or decreasing temporal trends for herbicides, fungicides, or insecticides. Although the total mass of herbicides remained relatively consistent, there was a significant change in the types of herbicides applied associated with the increased adoption of GMHT-canola; the most significant trend being the increase of GLY, from 16% to 45% of the total herbicides used in 1996 to 2006, respectively. HRIs demonstrated that herbicides used in 2006, are on average, more soluble, but less persistent, less volatile, and less acutely toxic to mammals (inhalation and acceptable daily intake), aquatic invertebrates, fish and algae, than those applied in 1996. Although 2,4-D remains one of the top 10 herbicides applied to agricultural crops in Manitoba, there were no significant increasing or decreasing trends in 2,4-D use between 1996 and 2006. Results from the experimental studies revealed that 2,4-D mineralization half-lives (DT50) in soil varied from 3 days to 51 days with the total 2,4-D mineralization (MT) ranging from 5.8 to 50.9%, depending on soil moisture, temperature, slope position, and depth. Both DT50 and MT demonstrated a polynomial relationship with temperature, typical of a biological system with minimum, optimum, and maximum temperatures.
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 | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".