Conservation value of national forest roadless areas
Bibliographic record
Abstract
Abstract Conservation scientists call for establishing additional protected areas amidst ongoing threats of expanding human development. Nevertheless, some existing protected areas are being downsized and demoted of their existing conservation protections. In 2001, the Roadless Area Conservation Rule prohibited road construction and timber harvest in 240,000 km 2 of inventoried roadless areas (IRAs) located on United States Department of Agriculture Forest Service lands. IRAs represent a non‐legislative protected status that is in jeopardy of conservation demotion or “degazettement,” and few national protected area assessments recognize the IRA designation. Since the rule's conception two decades ago, little research has been conducted to assess the conservation values of IRAs and the values they could add to the existing system of highly protected areas in the continental United States. To increase understanding of these conservation values, we assessed three aspects of roadless areas: (a) how wild and intact are IRA lands compared to state, national, and protected lands, (b) how do IRAs complement the size, connectedness, and representation of protected lands, and (c) how do IRAs contribute to protection of important ecosystem services (drinking water and annual carbon capture)? Through this analysis we found that many IRAs are among the most wild, undeveloped areas both in the nation and within their respective states. IRAs increase the size of—and reduce isolation between—protected areas, likely buffering them from external stressors. In some places, IRAs protect watersheds that deliver drinking water to hundreds of thousands of people. IRAs also add significantly to the total carbon captured by existing protected areas. The results of our evaluation demonstrate the potential of IRAs to contribute to the conservation value of the U.S. protected area system and to deliver important ecosystem services.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".