Spatial socioeconomic data as a cost in systematic marine conservation planning
Bibliographic record
Abstract
Abstract A common objective in identifying conservation areas is to minimize conservation costs while achieving a set of conservation targets. Recent literature highlights the importance of incorporating socioeconomic costs into conservation planning. Here, we review how costs have been used in systematic marine conservation planning. Four approaches emerged from the literature: (1) uniform cost or area as a proxy for human use, (2) opportunity costs, (3) multiple socioeconomic costs, and (4) measures of naturalness or ecological impact of human activities. Most marine systematic conservation planning projects that used a spatially explicit socioeconomic cost focused on fisheries as the opportunity cost. No study has incorporated transaction or management costs into the design of marine protected areas using systematic conservation planning software. Combining multiple costs into one cost is one of the primary challenges of incorporating socioeconomic costs into conservation planning decision support tools. Combining many costs is feasible when each cost is measured in the same unit (e.g., dollars), but this information is rarely available in marine planning. Where the objective of the planning exercise is to minimize impacts on multiple stakeholder groups, the use of separate scenarios or multi‐zone software may be a viable option.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".