Assessing connectivity and the contribution of private lands to protected area networks in the United States
Bibliographic record
Abstract
Current targets for protected area network coverage call for increased protection but lack specificity in terms of criteria for parcel type, placement, and landscape connectivity. We assessed land conservation achieved by protected area networks in the contiguous United States, and assessed whether private lands contributed substantially to network coverage and connectivity given species dispersal abilities. On average, states have 4.1% (range: 0.2% to 15.8%, n = 48) protected area coverage with connectivity ≤10 km. Terrain ruggedness, percent farmland, and population density are the primary determinants of protected area placement, leading to biased representation of land features currently under protection. On average, private protected areas contribute <1.1% (range 0.0% to 9.2%) to current protected area coverage across the United States. We conclude that current levels of protection and connectivity are inadequate to support a functional protected area network, and that increased and strategic selection of private protected areas is necessary to meet conservation planning goals.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".