Testing Integrated Management Strategies for Tall Buttercup (<i>Ranunculus acris</i>) in Irrigated Hayfield Meadows
Bibliographic record
Abstract
Abstract Tall buttercup is an invasive forb that has been reported in all but eight states and one Canadian province. The species has been of concern in Montana where it has invaded over 8,300 ha, and it has been particularly problematic in irrigated hayfield meadows that are used for forage production. This study sought to develop an integrated management strategy to control tall buttercup while maintaining forage production. Research was conducted over 2 yr at flood-irrigated and subirrigated hayfield meadows near Twin Bridges, MT. Treatments were randomly applied in a split-plot design with four replications at both sites. Herbicide treatments occurred at the whole-plot level: nonsprayed, aminopyralid (172 g ai ha −1 ), aminocyclopyrachlor + chlorsulfuron (83 g ai ha −1 + 33 g ai ha −1 ), and dicamba (981 g ai ha −1 ). Split plots consisted of mowing and fertilization (28 kg N ha −1 ). All herbicides provided up to 2 yr of tall buttercup control at both sites. In the second year, aminocyclopyrachlor + chlorsulfuron and aminopyralid reduced tall buttercup by 93% and 96%, respectively, for the subirrigated and flood-irrigated sites. At the subirrigated site, mowing reduced tall buttercup by 71%, and fertilization reduced it by 57%. Forage decreased following aminocyclopyrachlor + chlorsulfuron treatments. The integration of herbicide, mowing, and fertilization did not improve tall buttercup control.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".