Can Wide Consultation Help with Setting Priorities for Large-Scale Biodiversity Monitoring Programs?
Bibliographic record
Abstract
Climate and other global change phenomena affecting biodiversity require monitoring to track ecosystem changes and guide policy and management actions. Designing a biodiversity monitoring program is a difficult task that requires making decisions that often lack consensus due to budgetary constrains. As monitoring programs require long-term investment, they also require strong and continuing support from all interested parties. As such, stakeholder consultation is key to identify priorities and make sound design decisions that have as much support as possible. Here, we present the results of a consultation conducted to serve as an aid for designing a large-scale biodiversity monitoring program for the province of Québec (Canada). The consultation took the form of a survey with 13 discrete choices involving tradeoffs in respect to design priorities and 10 demographic questions (e.g., age, profession). The survey was sent to thousands of individuals having expected interests and knowledge about biodiversity and was completed by 621 participants. Overall, consensuses were few and it appeared difficult to create a design fulfilling the priorities of the majority. Most participants wanted 1) a monitoring design covering the entire territory and focusing on natural habitats; 2) a focus on species related to ecosystem services, on threatened and on invasive species. The only demographic characteristic that was related to the type of prioritization was the declared level of knowledge in biodiversity (null to high), but even then the influence was quite small.
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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 | 0.142 | 0.246 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.033 | 0.007 |
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".