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Record W3094130475 · doi:10.1017/wet.2020.115

Flumioxazin soil persistence under plastic mulch and effects of pretransplant applications on strawberry

2020· article· en· W3094130475 on OpenAlexaff
Nathan S. Boyd, Shaun M. Sharpe, Ramdas Kanissery

Bibliographic record

VenueWeed Technology · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsPlastic mulchMulchWeed controlSowingAgronomyPersistence (discontinuity)WeedShootCultivarHorticultureBiology

Abstract

fetched live from OpenAlex

Abstract Weeds are managed in Florida strawberry production systems with plastic mulches, fumigants, and herbicides. There are limited post-transplant options to control weeds that emerge in the planting holes in the plastic-covered beds, but flumioxazin at 107 g ai ha −1 can be applied pretransplant under the plastic mulch to control broadleaf and grass weeds. Three research trials were conducted in Balm and Dover, FL, in 2017 and 2018 to evaluate tolerance of the strawberry cultivar ‘Radiance’ to flumioxazin rates ranging from 54 to 6,854 g ha −1 and to estimate herbicide persistence under the plastic mulch. Shoot damage was observed at 428 to 857 g ha −1 (4× and 8× the label rate, respectively), but a significant increase in the number of dead plants was not observed until the treatment rate was 857 g ha −1 at one site and 3,427 g ha −1 at a second site (8× and 32× the label rate, respectively). Berry yields were unaffected by rates lower than 857 g ha −1 . Flumioxazin persisted throughout the growing season (approximately 150 d) with no reduction in soil concentration. We conclude that applied at the label rate, flumioxazin is a safe pretransplant weed management option for season-long weed control in strawberry with no yield reduction at rates below 8× the label rate. Caution is recommended for growers who plant a second crop on the same bed.

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)0.000
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 score0.616
Threshold uncertainty score0.181

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.011
GPT teacher head0.187
Teacher spread0.176 · 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

Citations6
Published2020
Admission routes1
Has abstractyes

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