Tires as larval habitats for mosquitoes (Diptera: Culicidae) in southern Manitoba, Canada
Bibliographic record
Abstract
In 2003, a survey at waste management grounds and tire dealerships was conducted to determine the species composition of mosquitoes in tires in southern Manitoba, Canada. Over 25% of the 1,142 tires sampled contained a total of 32,474 mosquito larvae and pupae. Culex restuans made up at least 95% of the larvae collected for each month of the summer. Culiseta inornata and Culex tarsalis reached their greatest numbers in July and August, respectively, though they were never abundant. Ochlerotatus triseriatus was also found but never reached more than 1% of the total larvae collected in any given month. Mosquito prevalence was more than three times greater in August (36.1%) than in June (11.7%). Orientation affected prevalence of mosquitoes in tires: 31.4% of vertical tires (tires standing on their treads) contained mosquitoes, whereas mosquitoes were found in only 18.9% of horizontal tires (tires parallel to the ground). Tires in the eastern region of Manitoba contained mosquitoes more often (61.7%), irrespective of date, than Winnipeg (25.9%), the central region (29.1%), or the western region (19.8%). Mosquito prevalence was similar across three size categories of tires, car tires (18.8%), truck tires (19.8%), and semi-trailer tires (26.7%), though tractor tires (47.8%) contained significantly more mosquitoes than tires in the other categories.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".