Accumulated Organic Debris in Catch Basins Improves the Efficacy of S-Methoprene Against Mosquitoes in Toronto, Ontario, Canada
Bibliographic record
Abstract
To control West Nile Virus in the greater Toronto area of Ontario, Canada, S-methoprene (Altosid XRbriquets 2.1% AI) is applied each year to storm water catch basins. Because the efficacy of the XRbriquets to reduce adult mosquito populations had not been evaluated locally and was influenced by organic debris in a pilot study, we compared the efficacy of the briquets in 17 sediment and debris-filled catch basins versus 20 catch basins that were vacuumed free of debris. Emergence rates approached 100% in the 5 untreated control catch basins. Emergence rates were significantly lower, and S-methoprene was detected more often and at higher levels, in debris-filled basins versus cleaned catch basins. Overall, 20% of pupae emerged from clean catch basins versus only 3% from debris-filled ones, the difference between treatments becoming significant after 26 days. S-methoprene and total organic carbon concentrations in the catch basins were positively correlated (P < 0.001). We hypothesize that S-methoprene is binding to the organic fraction in the water and sediment in the debris-filled basins, prolonging S-methoprene doses, which are reflected in lower mosquito emergence rates.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".