Quantifying the value of participatory science data for conservation decision‐making
Bibliographic record
Abstract
Abstract Monitoring biodiversity can be critical for informing effective conservation strategies, but can also deplete the resources available for management actions if the time and money available for both activities are limited. Freely available participatory science data may help alleviate this issue, but only if data quality is sufficient to inform the best decisions. Our objective was to quantify the predicted outcomes of prioritizing conservation action based on regional participatory science compared to using targeted professional monitoring data. Using data from the BirdReturns program in the Central Valley of California as a case study, we prioritized properties for conservation action based on the predicted probability of detecting seven shorebird species, using a range of budgets as constraints. For prioritizations conducted using data from the professional surveys, the cost of monitoring was deducted from the total budget available for conservation action. Crowd‐sourced data performed better than professional data even before accounting for the cost of professional monitoring, and substantially better when monitoring costs were explicitly considered. The difference in performance was particularly stark at lower budgets, where the professional monitoring data consumed a substantial proportion of the budget. Synthesis and applications. Prioritizing conservation action based on high‐quality, freely available participatory science data could theoretically result in better biodiversity outcomes than paying for targeted professional monitoring, allowing managers to redistribute limited conservation resources from monitoring to action.
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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 | 0.137 | 0.408 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".