A simple empirical model to predict forest insecticide ground-level deposition from a compendium of field data
Bibliographic record
Abstract
Deposit data from 205 aerial forest insecticide applications conducted in field trials by the Canadian Forest Service, Great Lakes Forestry Centre over a 15-year period are summarized. Deposit measurements were taken under "worst case" scenarios in the sense that direct applications were made over water bodies, and ground samplers were intentionally placed in open or cleared areas of forest. The median % deposit on shoreline collectors (32 separate applications) was 5.7%, on mid-stream collectors (44 separate applications) was 6.2%, and on forest floor collectors (129 separate applications) was 4.9%. Forest floor deposit was most closely associated with application rate and droplet size (r = 0.624, p < 0.001 and r = 0.662, p = 0.011, respectively) but these variables combined only explained 44% of the variation in deposit. Data from all three collector types were grouped by 10% deposit increments and combined to provide a data set from all deposition scenarios. A negative exponential model was fitted to the proportion of these combined sites regressed on % deposit in 10% increments and plotted as a deposit probability distribution curve (p < 0.001, r2 = 0.992). The probability distribution curve indicated that 5-10% deposit would be expected about 57-91% of the time, whereas 50% deposit or greater would be expected about 2% of the time or less. In a probabilistic risk assessment for aerially applied insecticides in a conifer-dominated forest environment, the probability distribution curve based on empirical data presented here can be used to refine the characterization of exposure scenarios from which effects estimates can be derived.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".