Mind the <scp>GAP</scp> —But make it better: Improving the U.S. Gap Analysis Project's protected‐area classification system to better reflect biodiversity conservation
Bibliographic record
Abstract
Abstract Protected areas are foundational to the conservation of biological diversity, and many scientists have called for increased protections in the face of the current extinction and climate‐change crises. Currently in the United States, the most recognized way to track the amount of protected land is the Gap Analysis Project classification system, which we argue is deficient in three ways: it does not, in a systematic way, specify the typical uses and constraints associated with each conservation designation; it is not fine‐tuned or nuanced enough to distinguish the levels of protection among designations within “protected” or “unprotected” areas that allow quite different human activities; and it largely ignores the durability of the designations, failing to account for an area's vulnerability to downsizing, downgrading, or degazetting. We propose a new classification system to address these deficiencies and demonstrate this method for several of the most common federal land designations in the United States.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".