Sharing Opportunistic Observations of Insects Provides Value for Pest Monitoring and Management in North America
Bibliographic record
Abstract
Abstract iNaturalist and other technology-enabled biodiversity recording applications allow individuals to easily capture and share biodiversity data. Built in machine-learning algorithms facilitate initial identification of the observed taxon, which is subsequently refined, corrected, or validated by the community of users. With hundreds of millions of records in iNaturalist alone, there is enormous potential to use this data for understanding where species occur in space and time. Insects (including species that can act as pests) are a commonly observed taxon within these databases. Numerous end-users are finding ways to effectively use this data to study and better understand pest insects. Here, we share three case studies that demonstrate the power of community-science data from iNaturalist in advancing science. We argue that in order to maintain a sustainable system of community science to serve these purposes, the following conditions must be met: excellent user-experience of the technology must be upheld, a welcoming and supportive atmosphere is maintained within the community of users, and that the efforts of contributors are formally recognized. Information © The Authors 2024
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".