Bibliographic record
Abstract
eButterfly was created in 2011 based upon two simple ideas. First, many people are passionate about observing butterflies and, second, every butterfly observation has potential research value for fundamental and conservation research. Even observations of common species from well-sampled areas have value for monitoring population change, phenology, and for other spatiotemporal studies. Using informal science education, eButterfly steers participants into standardized data collection and provides extensive resources to improve observers’ capacities for butterfly detection and identification. eButterfly users document the presence or presumed absence of species as well as abundance through checklist data collection. To report butterfly observations, a web interface engages participants to submit observations through three interactive steps designed to collect location, effort, and the species and numbers detected. eButterfly encourages participants to submit photos of their observations as vouchers for species verification. eButterfly - an international, data driven project dedicated to butterfly biodiversity, conservation, and education - is a joint initiative of the Insectarium de Montréal - Espace pour la vie, Vermont Center for Ecostudies, Mila - Quebec Artificial Intelligence Institute, and University of Ottawa.Through time, each participant, each observation, each checklist, and each verification builds the database. eButterfly then shares this treasure trove of butterfly data with a global community of community scientists, educators, lepidopterists, conservationists, and land managers. In time, this information will become the foundation for a better understanding of butterfly distribution and population trends.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.088 | 0.019 |
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".