Plant Diversity Affects Performance of Invasive Thistles in Restored Nebraska Grasslands
Bibliographic record
Abstract
Invasive plant species threaten native grasslands, affecting nutrient cycling, biodiversity, wildlife habitat, and usable land for production. Consequently, preventing establishment of invasive species is critical before removal becomes difficult and expensive. The purpose of this study was to examine the effects of grassland plant diversity on musk thistle (Carduus nutans) and Canada thistle (Cirsium arvense) establishment and determine which environmental factors (light penetration, soil moisture, plant diversity, and soil nitrogen) account for resistance to invasions. In a field experiment at The Nature Conservancy’s Wood River site (Nebraska), the two invasive thistle species were planted into replicated grassland diversity plots. The 0.3 hectares grassland plots were seeded as monoculture (Andropogon gerardi), low diversity, and high diversity treatments in 2010. The experiment also included plots maintained as bare soil. Plant diversity was measured in 2013. Environmental factors were measured during the growing seasons (April-October) of 2013 and 2014. After two years, both thistle species flourished in bare soil plots, maintained populations in monoculture and low diversity plots, while thistles in the high diversity grassland plots emerged but died prior to completing their normal life cycle. Analyses of the environmental factors show strong declines in resource availability (light, water, nitrogen) associated with both plant biomass and diversity across the experimental diversity gradient. Adviser: David A. Wedin
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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 | 0.000 |
| 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".