An interoperable and standardized protocol for reporting systematic conservation planning projects
Bibliographic record
Abstract
Abstract Systematic conservation planning ( SCP ) is an operational and scientific framework that assists in deciding where, how, and when to implement conservation intervention. Studies using SCP approaches have proliferated due to their immediate relevance for applied conservation. For example, they can help identify cost‐effective opportunities for expanding areas under conservation management to achieve high‐level policy goals such as those of the Global Biodiversity Framework. Yet SCP can be conducted in various ways, and results can vary depending on problem formulation, parameterizations, contexts, and prioritization approaches. There is a need to facilitate comparison of SCP studies to understand key criteria and assumptions made in the planning process. Here, we propose a standardized reporting protocol for SCP that is readily applicable across study aims, realms, and spatial scales. The new Overview and Design Protocol for Systematic Conservation Planning ( ODPSCP ) describes the key steps from the design to the computational stages of SCP . It enables researchers, scientific editors, and decision‐ and policymakers to assess the scope and comprehensiveness of SCP exercises. To facilitate uptake and ease of reporting, the protocol is openly available through an interactive web interface and which can be further enhanced following methodological advancements in conservation planning. We encourage the conservation community to adopt the reporting protocol to promote transparency and reproducibility, standardized reporting as well as facilitate peer review and independent evaluation.
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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 | 0.538 | 0.611 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.019 | 0.014 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.008 | 0.013 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.027 | 0.018 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".