Investigating the prevalence of animal bites and stings in Kashan city in 2024
Bibliographic record
Abstract
AbstractA 25-question questionnaire was used to collect information on animal bites and stings in urban and rural areas and then the data were analyzed. Overall, 87% of the 500 individuals surveyed reported having been bitten and stung by various animals. Among the participants, 64.05% (278 individuals) had been bitten and stung only once, while 35.95% (156 individuals) had experienced two or three bites and stings. Among these, 38.5% of the bites and stings were from Culicidae mosquitoes, 33.5% from bees, 6.5% from scorpions, 4% from sandflies, 4% from centipedes, 3.5% from cats, 3% from fleas, 2.5% from dogs, 1.25% from spiders, 1.25% from mice, 1% from snakes, and 1% from bird pecks. Most individuals encountered animals and were bitten and stung in and around their homes, with the highest frequency of bites and stings being from insects such as mosquitoes and bees. Raising awareness about the life cycles of these arthropods, improving environmental conditions, and implementing safe preventive measures can significantly decrease the occurrence and frequency of animal stings and bites.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".