Late-Season Targeted Grazing of Yellow Starthistle (<i>Centaurea solstitialis</i>) with Goats in Idaho
Bibliographic record
Abstract
Abstract Yellow starthistle (Centaurea solstitialis) is an exotic winter annual forb that is aggressively invasive and problematic in much of California, Idaho, Oregon, and Washington. Yellow starthistle control is particularly challenging in canyon rangelands where accessibility limits control options. Our objective was to evaluate the effects of late-season targeted goat grazing on yellow starthistle and nontarget grasses and forbs. A 3-yr grazing study was initiated in 2006 on a 380-ha (939 acres) canyon grassland site infested with yellow starthistle near White Bird, ID. Twenty-four paired plots were established, with each pair including a fenced subplot to exclude grazing and a similar-sized adjacent subplot that was grazed. Density of yellow starthistle plants and seedheads was assessed after grazing of each plot in all 3 yr and before grazing in the second and third years. Canopy cover of yellow starthistle, grasses, and forbs also was measured. Grazed subplots had 58% fewer yellow starthistle plants than the ungrazed controls after grazing was applied and 94% fewer seedheads after 3 yr of grazing. Cover of yellow starthistle did not differ between grazed and ungrazed subplots after grazing in 2006, whereas grazing decreased yellow starthistle cover in 2007 and 2008 by about 75%. Goat grazing had little impact on canopy cover of grasses and resident forbs, with the exception of after grazing in 2007 when there was less forb cover in grazed areas compared with ungrazed areas. Late-season (i.e., July to November) targeted goat grazing appears to be an effective way to reduce yellow starthistle plant densities at landscape scales, which creates a large window of opportunity for grazing treatment and flexibility for land and livestock managers.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".