A review of applications of the six-step method of systematic conservation planning
Bibliographic record
Abstract
Systematic Conservation Planning (SCP) is an approach to protected areas planning that follows a step-by-step process. Recent reviews have examined the use of key “biogeographic-concepts”, but an assessment of their use or effectiveness has not been done. We conducted a review of the literature on SCP to assess how the 6-step approach considers these concepts. Most of the 127 papers we reviewed varied in their application of SCP steps. Our findings suggest that protected areas plans are not effectively achieving conservation goals. Only six papers considered data uncertainty. Twenty papers used so-called “data free” conservation targets without clear rationales, and which have been shown to under-represent natural features. The median size of planning units applied (2500 ha) is too small to meet minimum area requirements for many species. We show how an examination of the variation in the ways that SCP is applied helps to identify best practices for achieving conservation effectiveness and efficiency. However, very few SCP efforts have been implemented, making it difficult to assess their effectiveness or efficiency in practice. Detailed examination of how SCP is implemented (perhaps focused on a specific region) can lead to a better understanding of how best to achieve large-scale conservation goals.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".