Incorporating holistic methodologies in determining wind resource availability for the Confederated Salish and Kootenai Tribes
Bibliographic record
Abstract
We have carried out site feasibility and desirability assessments for wind development on the reservation of the Confederated Salish and Kootenai Tribes (CSKT), located in Western Montana, USA working in collaboration with tribal experts and leaders. Holistic review of siting options includes technical, economic, environmental, and aesthetic factors. We have combined publically available GIS data for wind power classifications, digital elevation maps, and built infrastructure, with input from tribal planners to identify Seepay Ridge as a key technically and economically favored site. Preliminary go/no-go environmental assessments have been carried out by others. A major goal for this work was to provide new geospatial mapping methods and site indexing tools to enable individual communities and the reservation as a whole to assess the perceived visual impacts of specific wind development sites. The visual impact of wind development site j on any given location i was computed using a model from the literature, combined with line-of-sight viewshed modeling. We map the expected visual impact of a Seepay Ridge wind farm with either 10 or 50 turbines on locations across the reservation. The aggregated visual impact from towns on the reservation was also computed based on a population weighting. We found that a 50-turbine Seepay Ridge development has an Aggregated Urban Index (AUI) of 0.05. This means the development has about 5% of the visual impact of building a 50-turbine farm adjacent to each town on the reservation. We also conjecture that situational visual impact is an important, but as of yet unmeasured, viewshed impact variable. The perceived impact of a wind development is likely to be situation-dependent, since the perception of impact by an observer in an urban area vs. a cultural, wilderness, or primitive area is likely to be different for the same observer.
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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 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".