Strengths and limitations of using participatory science data to characterize a wildlife mass mortality event
Bibliographic record
Abstract
Abstract Participatory science (i.e., “community science” or “citizen science”) platforms are increasingly used at every level of ecological and conservation research, including disease monitoring. Here, we used a comprehensive, ground‐truthed mortality dataset to judge how well participatory science data from iNaturalist represented the magnitude, taxonomic, temporal, and spatial patterns of waterbird mortality associated with a mass mortality event following the incursion of highly pathogenic avian influenza in eastern Canada in 2022. The iNaturalist dataset was effective at identifying species with high mortality (especially Northern Gannets, Morus bassanus ), along with the time period and spatial regions with high concentrations of avian deaths. However, iNaturalist data severely underestimated the magnitude, overestimated the taxonomic breadth, and poorly represented the full geographic scope of disease‐related deaths. Our results suggest iNaturalist can be used to identify the species, timing, and location of relatively high mortality in situations where no other information is available and to supplement conventional sources of data. However, iNaturalist alone can neither quantify the magnitude nor pinpoint the mechanisms of mortality and therefore is not a viable substitute for comprehensive mortality assessments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".