Historical pesticide applications for the treatment of eastern spruce budworm infestations in New Brunswick
Bibliographic record
Abstract
Pesticides have been used in Canada since 1945 as part of large-scale aerial spray applications to control insect pests on forested lands. Some of the pesticides used historically were efficacious, nonselective, persistent, and have led to serious impacts on the environment. A well known, and extensively documented example is the large-scale aerial spray programs in New Brunswick, Canada. From 1952 to 1993, 97% of the 6.2 million ha of the forested lands of New Brunswick were treated with at least one application of one insecticide, the majority of which were applied to control outbreaks of eastern spruce budworm (Choristoneura fumiferana). The most well known insecticide was dichlorodiphenyltrichloroethane (DDT), applied from 1952 to 1968, which still persists in treated soils and adjacent water bodies, and caused the individual and cumulative ecosystem effects that can still be measured today. The insecticides that replaced DDT were nonpersistent and unlikely to be found today. However, during the years of application some of the insecticides were likely to have impacted local ecosystems to some degree. To aid future studies on the efficacy and environmental impact of these insecticides we created a digital spatial data set of known pesticide application in New Brunswick forestry from 1952 to 1993. The data set includes active ingredient, formulation, application rate, tank mix, aircraft type, and other ancillary information. The current version of the data is available on the New Brunswick Department of Natural Resources and Energy Development, GIS Open Data Page and in the supplemental material. Use of the data set for academic and educational purposes is encouraged, provided that both this data paper and the data source are properly cited; the Government of New Brunswick should be acknowledged as the data source (Open Government License http://www.snb.ca/e/2000/data-E.html).
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".