A plea for quantitative targets in biodiversity conservation
Bibliographic record
Abstract
Ecological degradation is both ubiquitous and relentless. Human activities have left a footprint even in the most remote locations. Some species benefit from certain forms of degradation whereas many others are expected to decline to extinction under current or increasing land-use intensity (Vitousek et al. 1997; Norris and Pain 2002). While the optimal allocation of conservation efforts and funding at the global scale is being debated (Myers et al. 2000; Balmford et al. 2002; O'Connor et al. 2003; Lamoreux et al. 2006), target setting at the landscape scale should be viewed as equally important because, for many taxa, this is the scale over which most human activities take place and management regulations are applied. A landscape can be defined as a mosaic of habitat types whose extent reflects the perspective of target species or taxa (Wiens et al. 2002). However, it should be noted that this organism-centered perspective of the landscape must interact with human perception and action. Forest managers perceive the landscape as that of the “forest” or “forest management unit”, which may cover hundreds of square kilometers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".