Bibliographic record
Abstract
This study developed a dynamic two-big-leaf multilayer pesticide emission model (PeCM) to simulate pesticide loss to the atmosphere from crop canopy. The pesticide volatilization, leaf residue and washoff, as well as rainfall interception, evaporation and transpiration, were considered in the model to help simulate the whole process of pesticide canopy emission. The PeCM was further incorporated into a pesticide runoff loss model (PeLM), which was previously developed by the author to simulate pesticide loss through surface runoff and soil erosion. To verify the feasibility of the developed model, a case study was conducted in the Auglaize-Blanchard Watershed in Ohio. The results demonstrated that the PeCM was able to simulate pesticide emission to the atmosphere. Furthermore, to investigate the performance of the modified PeLM, the modeling outputs were compared with the observed data as well as the outputs of the PeLM. The results indicated that the modified PeLM had advantages in accounting for more pesticide transport processes and improving the simulation accuracy.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".