A Spatial Model to Prioritize Sagebrush Landscapes in the Intermountain West (U.S.A.) for Restoration
Bibliographic record
Abstract
Abstract The ecological integrity of Sagebrush ( Artemisia spp.) ecosystems in the Intermountain West (U.S.A.) has been diminished by synergistic relationships among human activities, spread of invasive plants, and altered disturbance regimes. An aggressive effort to restore Sagebrush habitats is necessary if we are to stabilize or improve current habitat trajectories and reverse declining population trends of dependent wildlife. Existing economic resources, technical impediments, and logistic difficulties limit our efforts to a fraction of the extensive area undergoing fragmentation, degradation, and loss. We prioritized landscapes for restoring Sagebrush habitats within the intermountain western region of the United States using geographic information system (GIS) modeling techniques to identify areas meeting a set of conditions based on (1) optimum abiotic and biotic conditions favorable for revegetation of Sagebrush; (2) potential to increase connectivity of Sagebrush habitats in the landscape to benefit wildlife; (3) location of population strongholds for Greater Sage‐Grouse ( Centrocercus urophasianus , a species of conservation concern); and (4) potential impediments to successful restoration created by Cheatgrass ( Bromus tectorum , an invasive exotic annual grass). Approximately 5.8 million ha in southwestern Idaho, northern Nevada, and eastern Oregon met our criteria for restoring Wyoming big sagebrush ( Artemisia tridentata ssp. wyomingensis ) and 5.1 million ha had high priority for restoring Mountain big sagebrush ( A. tridentata ssp. vaseyana ). Our results represent an integral component in a hierarchical framework after which site‐specific locations for treatments can be focused within high‐priority areas. Using this approach, long‐term restoration strategies can be implemented that combine local‐scale treatments and objectives with large‐scale ecological processes and priorities.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".