Is U.S. climatic diversity well represented within the existing federal protection network?
Bibliographic record
Abstract
Establishing protection networks to ensure that biodiversity and associated ecosystem services persist under changing environments is a major challenge for conservation planning. The potential consequences of altered climates for the structure and function of ecosystems necessitates new and complementary approaches be incorporated into traditional conservation plans. The conterminous United States of America (CONUS) has an extensive system of protected areas managed by federal agencies, but a comprehensive assessment of how this network represents CONUS climate is lacking. We present a quantitative classification of the climate space that is independent from the geographic locations to evaluate the climatic representation of the existing protected area network. We use this classification to evaluate the coverage of each agency's jurisdiction and to identify current conservation deficits. Our findings reveal that the existing network poorly represents CONUS climatic diversity. Although rare climates are generally well represented by the network, the most common climates are particularly underrepresented. Overall, 83% of the area of the CONUS corresponds to climates underrepresented by the network. The addition of some currently unprotected federal lands to the network would enhance the coverage of CONUS climates. However, to fully palliate current conservation deficits, large-scale private-land conservation initiatives will be critical.
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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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.026 | 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; both teacher heads agree on what is shown here.
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".