Spatial modelling of biodiversity conservation priorities in Portugal’s <i>Montado</i> ecosystem using Marxan with Zones
Bibliographic record
Abstract
Summary Spatial models are increasingly being used to target the most suitable areas for biodiversity conservation. This study investigates how the spatial tool Marxan with Zones (MARZONE) can be used to support the design of cost-effective biodiversity conservation policy. New in this study is the spatial analysis of the costs and effectiveness of different agro-environmental measures (AEMs) for habitat and biodiversity conservation in the Montado ecosystem in Portugal. A distinction is made between the financial costs paid to participating landowners and farmers for adopting AEMs and the broader economic opportunity costs of the corresponding land-use changes. Habitat and species conservation targets are furthermore defined interactively with the local government agency responsible for the management of protected areas, while the costs of agro-forestry activities and alternative land uses are estimated in direct consultation with local landowners. MARZONE identifies the spatial distribution of priority areas for conservation and the associated costs, some of which overlap with existing protected areas. These results provide useful insights into the trade-offs between nature conservation and the opportunity costs of protecting ecologically vulnerable areas, helping to improve current and future conservation policy design.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".