Wilderness forms and their implications for global environmental policy and conservation
Bibliographic record
Abstract
With the intention of securing industry-free land and seascapes, protecting wilderness entered international policy as a formal target for the first time in the zero draft of the Post-2020 Global Biodiversity Framework under the Convention on Biological Diversity. Given this increased prominence in international policy, it is timely to consider the extent to which the construct of wilderness supports global conservation objectives. We evaluated the construct by overlaying recently updated cumulative human pressure maps that offer a global-scale delineation of industry-free land as wilderness with maps of carbon stock, species richness, and ground travel time from urban centers. Wilderness areas took variable forms in relation to carbon stock, species richness, and proximity to urban centers, where 10% of wilderness areas represented high carbon and species richness, 20% low carbon and species richness, and 3% high levels of remoteness (>48 h), carbon, and species richness. Approximately 35% of all remaining wilderness in 2013 was accessible in <24 h of travel time from urban centers. Although the construct of wilderness can be used to secure benefits in specific contexts, its application in conservation must account for contextual and social implications. The diverse characterization of wilderness under a global environmental conservation lens shows that a nuanced framing and application of the construct is needed to improve understanding, communication, and retention of its variable forms as industry-free places.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".