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. The landscapes we tend to envision when considering human activities such as timber harvesting or agriculture may match those perceived by many birds and mammals, but not those over which the dynamics of most species (e.g. plants and insects) take place. With the exception of some mega-projects, most human activities tend to alter relatively small patches (e.g. a forest stand or a field).
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".