Wild, connected, and diverse: building a more resilient system of protected areas
Bibliographic record
Abstract
Current systems of conservation reserves may be insufficient to sustain biodiversity in the face of climate change and habitat losses. Consequently, calls have been made to protect Earth's remaining wildlands and complete the system of protected areas by establishing conservation reserves that (1) better represent ecosystems, (2) increase connectivity to facilitate biota movement in response to stressors including climate change, and (3) promote species persistence within intact landscapes. Using geospatial data, we conducted an assessment for expanding protected areas within the contiguous United States to include the least human-modified wildlands, establish a connected network, and better represent ecosystem diversity and hotspots of biodiversity. Our composite map highlights areas of high value to achieve these goals in the western United States, where existing protected areas and lands with high ecological integrity are concentrated. We also identified important areas in the East rich in species and containing ecosystems that are poorly represented in the existing protected area system. Expanding protection to these priority areas is ultimately expected to create a more resilient system for protecting the nation's biological heritage. This expectation should be subject to rigorous testing prior to implementation, and regional monitoring will ensure areas and actions are adjusted over time.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".