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Record W2971001389 · doi:10.1017/wet.2019.71

Evaluation of herbicides for hair fescue (<i>Festuca filiformis</i>) management and potential seedbank reduction in lowbush blueberry

2019· article· en· W2971001389 on OpenAlex
Scott N. White

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWeed Technology · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsDalhousie University
Fundersnot available
KeywordsGlufosinatePruningPerennial plantBiologyAgronomyTuftGlyphosate

Abstract

fetched live from OpenAlex

Abstract Hair fescue is a widespread, seed-limited perennial grass in lowbush blueberry fields. Growers rely on pronamide, an expensive and difficult herbicide to use, for hair fescue management. Recent herbicide registrations provide opportunity to reduce pronamide use, though effects of these herbicides on hair fescue suppression and seedbank reduction are not well understood. The objectives of this research were to determine (1) the effects of herbicides currently registered in lowbush blueberry on suppression of hair fescue tufts and (2) whether suppression of hair fescue with these herbicides reduces hair fescue seedbanks. Pronamide gave the most consistent reductions in flowering tuft density, though applications after both autumn pruning and autumn of the nonbearing year were required to reduce the hair fescue seedbank by &gt;60% across sites. Nonbearing-year hexazinone applications did not control hair fescue or reduce the seedbank. Nonbearing-year terbacil applications reduced flowering tuft density, but hair fescue recovered in the bearing year, and the seedbank was not reduced. Glufosinate applications following autumn pruning or in the spring of the nonbearing year did not suppress hair fescue or reduce the seedbank. Spring nonbearing-year foramsulfuron applications, alone or after autumn or spring glufosinate applications, reduced hair fescue flowering tuft density, but hair fescue recovered in the bearing year, and the seedbank was not reduced. In contrast, autumn and spring glufosinate applications followed by spring nonbearing-year foramsulfuron applications, when combined with autumn nonbearing-year pronamide applications, reduced flowering tuft density in both the nonbearing and bearing years and reduced the hair fescue seedbank by 58% to 83% across sites. Results indicate that hair fescue seedbanks can be reduced in lowbush blueberry fields and that a reduction in pronamide use will require alternative bearing-year treatments to prevent tuft recovery and seed production.

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.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.841
Threshold uncertainty score0.192

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 head0.227
Teacher spread0.216 · 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