Scheduling incremental actions to build a comprehensive national protected area network for Papua New Guinea
Bibliographic record
Abstract
Abstract Systematic conservation planning identifies priority areas to cost‐effectively meet conservation targets. Yet, these tools rarely guide wholesale declaration of reserve systems in a single time step due to financial and implementation constraints. Rather, incremental scheduling of actions to progressively build reserve networks is required. To ensure this incremental action is guided by the original plan, and thus builds a reserve network that meets all conservation targets, strategic scheduling, and iterative planning is needed. We explore the issue of scheduling conservation actions using the national scale conservation plan for Papua New Guinea (PNG), commissioned by the PNG Conservation and Environment Protection Authority that identifies a comprehensive set of priority areas that meet conservation targets in both the land and sea. As part of the planning process a subset of areas were identified in collaboration as priorities for immediate action—termed areas of interest (AOIs). However, the extent to which targets are met if action stopped after implementing the AOIs is unknown. We test three possible implementation scenarios based on these priority areas to measure target achievement and shortfalls. We then consider how iterative planning would interact with scheduling actions to identify new long‐term priorities that will meet missing targets. Our results show that while a large number of conservation targets are met within the AOIs there are shortfalls for protecting threatened and range restricted endemic species. Meeting targets for these would require an updated set of national priorities and an additional 13% of land area compared with if all areas identified in the original assessment were protected in a single time step. This provides important insights into the benefits of strategic scheduling of implementation, as well as the need for capacity to monitor action and update priorities as implementation proceeds.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".