Factors Controlling Patterns of Canada Thistle (<i>Cirsium arvense</i>) and Yellow Starthistle (<i>Centaurea solstitialis</i>) Across the Cascade-Siskiyou National Monument
Bibliographic record
Abstract
Landscape patterns of broadleaved noxious weeds across the Cascade-Siskiyou National Monument are examined in the context of environmental and management factors to improve our understanding of weed dynamics. Environmental factors include a range of topographic edaphic variables, while management factors provide insight about historic vegetation manipulation, road construction and forage utilization by wildlife and livestock. Distribution patterns of Canada thistle (Cirsium arvense) and yellow starthistle (Centaurea solstitialis) across the Monument are best described by a combination of topographic, edaphic, biotic, and management factors. Variables incorporated within models describing landscape patterns of weeds varied with response variable (actual weed locations versus weed density at random locations throughout the landscape) and the incorporation of private lands, characterized by less intense or localized lack of weed surveys, with public lands. Optimization of data quality by restriction of analysis to public lands in a landscape context identified elevation, maximum forage utilization by livestock and native ungulates, and past management treatments as predictors common to both Canada thistle and yellow starthistle distribution. Additional variables associated with the pattern of Canada thistle included heat-load and soil depth. The optimal model describing yellow starthistle distribution also included soil classification as vertisol, NRCS ecological type, woody vegetation cover, and average utilization by livestock and native ungulates. Analysis of individual variables indicated that roads and distance from water influenced the distribution of weeds. The association between roads, water, and forage utilization implies a synergy between road construction, proximity to water, livestock and wildlife dispersion, with weed establishment.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".