Snap Decisions: Assessing Participation and Data Quality in a Citizen Science Program Using Repeat Photography
Bibliographic record
Abstract
Photo-point monitoring through repeat photography allows assessment of long-term ecosystem changes, and photos may be collected using citizen science methods. Such efforts can generate large photo collections, but are susceptible to varying participation and data quality. To date, there have been few assessments of the success of citizen science projects using repeat photography methods in meeting their objectives. We report on the success of the PhotoMon Project, a photo-point monitoring program at Pinery Provincial Park, Canada, at meeting its primary goals of affordably collecting seasonal reference photographs of significant ecosystems within the park, while providing a stewardship opportunity for park visitors. We investigated how the quantity of submitted photos varied over time (quantity), and how closely those photos matched the suite of criteria of the PhotoMon Project (quality). Photo submissions occurred year-round and at all sites, although a low proportion of park visitors participated in the program. Photo quantity varied among sites and seasonally, reaching a low during the winter, but with proportional participation in the project lowest in summer. Photo quality was consistent year-round, with most photos meeting most program criteria. Common issues with photo quality included photo lighting and orientation. We conclude that the program met its scientific goal of compiling seasonal reference photos, but that comparatively few park visitors engage in the program. We suggest changes to increase visitor motivation to participate, but recognize that these may compromise the program’s current affordability and ease of management.
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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 | 0.065 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".