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Record W1909773185 · doi:10.1614/ipsm-d-14-00066.1

Testing Integrated Management Strategies for Tall Buttercup (<i>Ranunculus acris</i>) in Irrigated Hayfield Meadows

2015· article· en· W1909773185 on OpenAlexfundaboutno aff
Hally Kirsten. Strevey, Jane M. Mangold

Bibliographic record

VenueInvasive Plant Science and Management · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and fungal interactions
Canadian institutionsnot available
FundersSyngenta Canada
KeywordsForageAgronomyHuman fertilizationEnvironmental scienceDicambaGrasslandWeed controlBiology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.552
Threshold uncertainty score0.380

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.077
GPT teacher head0.251
Teacher spread0.174 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2015
Admission routes2
Has abstractyes

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