“What’s This Bug?” Questions from the Public Provide Relevant Information on Species Distribution and Human–Insect Interactions
Bibliographic record
Abstract
In general, insects and arthropods polarizing: they either fascinate people, disgust people, or both, and they generate lots of questions. Museums are perceived as reliable sources of information and, as such, a go-to destination for the public to receive answers. Since its opening in 1990, the Montreal Insectarium has offered an entomological information service, allowing the public to send questions, photographs, and specimens for identification. All requests are answered by entomologists. Spatiotemporal variations in taxonomic, geographic, and thematic profiles of the 4163 requests received in 2010–2011 and 2017–2018 were analyzed. Requests came from 35 countries, and most of those requests came from Canada. The majority of requests were identification requests. Representing 25% of identification requests, the five most frequent species were the eastern dobsonfly Corydalus cornutus, the masked hunter Reduvius personatus, the giant water bug Lethocerus americanus, the western conifer-seed bug Leptoglossus occidentalis, and the Japanese beetle Popillia japonica. A comparison with the data from the citizen science platform iNaturalist shows that the EIS can be a valuable tool for invasive species detection. Frequent subjects included school projects, entomophagy (eating insects), and wasp and bee nests. Finally, we discuss the role of entomologists in providing scientific information but also in addressing common concerns regarding cohabitation with arthropods.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".