Conservation Businesses and Conservation Planning in a Biological Diversity Hotspot
Bibliographic record
Abstract
The allocation of land to biological diversity conservation competes with other land uses and the needs of society for development, food, and extraction of natural resources. Trade-offs between biological diversity conservation and alternative land uses are unavoidable, given the realities of limited conservation resources and the competing demands of society. We developed a conservation-planning assessment for the South African province of KwaZulu-Natal, which forms the central component of the Maputaland-Pondoland-Albany biological diversity hotspot. Our objective was to enhance biological diversity protection while promoting sustainable development and providing spatial guidance in the resolution of potential policy conflicts over priority areas for conservation at risk of transformation. The conservation-planning assessment combined spatial-distribution models for 646 conservation features, spatial economic-return models for 28 alternative land uses, and spatial maps for 4 threats. Nature-based tourism businesses were competitive with other land uses and could provide revenues of >US$60 million/year to local stakeholders and simultaneously help meeting conservation goals for almost half the conservation features in the planning region. Accounting for opportunity costs substantially decreased conflicts between biological diversity, agricultural use, commercial forestry, and mining. Accounting for economic benefits arising from conservation and reducing potential policy conflicts with alternative plans for development can provide opportunities for successful strategies that combine conservation and sustainable development and facilitate conservation action.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".