Implementation and Evaluation of a Communication Strategy to Control Ragweed Pollen
Bibliographic record
Abstract
The common ragweed (Ambrosia artemisiifolia) is widespread in southwestern areas of Quebec, Canada. It is known to release large quantities of pollen from July through September, triggering allergic reactions such as rhinitis and generating significant costs for public health. The objective of this study was to implement and evaluate a communication intervention aimed at decreasing ragweed pollen. Selected lands with potential ragweed presence were visited twice, before and after the intervention, on three seasons in the East of the Montreal Island, Quebec. At the first visit done in 2010, 2011, and 2012, ragweed plots were located and measured; at the second visit in 2012, the measures were redone. Various numbers of communications were sent to owners of ragweed-infested lands that included explanations of health impacts of ragweed pollen and the importance of mowing. Mixed logistic regressions were used to test the effect of the number of communications on the mow. In the group that received four notices, a statistically significant three-fold increase in the proportion of land owners that had cut ragweed plots (OR = 3.20; 95 %CI: 1.16-8.84) was noted, compared to the group that received only one notice. For owners of vacant lands, the effect was somewhat more pronounced (OR = 3.82; 95%CI: 1.23-11.67). Nonetheless, the change from one to three communications showed no increase of mowing. In conclusion, the results of the present study suggest that communications and reminders of the importance of ragweed cut to landowners could be an effective measure to limit ragweed pollen.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".