Scientific Evidence for Fifty Percent?
Bibliographic record
Abstract
A recent article by Dinerstein and colleagues (2017) presents an impressive and highly useful inventory of the global extent of protected area by ecozone. It also provides an assessment of the extent to which the planet has achieved a target of 50-percent protection, which the authors imply is an appropriate, science-based target for protected area coverage. We do not disagree with the value of protected areas as an important conservation strategy. However, we are concerned with the assumption implicit in the paper by Dinerstein and colleagues (2017) that the 50-percent target is scientifically derived and appropriate. Conservation biologists have long debated the question “how much protection is enough?” for conservation purposes. There is general agreement that the current Aichi target of 17 percent and the previous Brundtland Commission target of 12 percent are politically motivated targets and would not necessarily lead to a sufficient amount of land set aside from a scientific ecological perspective (Svancara et al. 2005).
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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 | 0.043 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.029 | 0.004 |
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".