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Record W2005074990 · doi:10.1614/wt-d-14-00098.1

Reduced Metribuzin Preharvest Interval on Potato Yield and Tuber Quality

2015· article· en· W2005074990 on OpenAlexaboutno aff
Peter J. Dittmar, Roger B. Batts, Katherine M. Jennings, Robin R. Bellinder, Stephen L. Meyers

Bibliographic record

VenueWeed Technology · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPotato Plant Research
Canadian institutionsnot available
Fundersnot available
KeywordsMetribuzinPreharvestHorticultureYield (engineering)AgronomyBiologyWeed controlPostharvest

Abstract

fetched live from OpenAlex

Metribuzin has a 60-d preharvest interval (PHI) in potato, which limits utility of metribuzin POST in potato. In certain years, the potato may not fully cover the area between the potato rows. This allows for late-season weed emergence and subsequent yield reduction through direct competition or harvest interference. Field experiments were conducted in 2011 at Castle Hayne, NC; Freeville, NY; Hasting, FL; and Plymouth, NC to determine the effect of a 30-d PHI on potato crop tolerance. The cultivars planted were ‘Superior' and ‘Yukon Gold' in Castle Hayne and Plymouth, ‘Castille' and Yukon Gold in Freeville, and ‘Atlantic' in Hastings. Treatments included metribuzin at 278 g ai ha −1 PRE, 30, and 60 d before harvest (DBH), and metribuzin at 556 g ha −1 at 30 and 60 DBH. Split application treatments included metribuzin at 556 g ha −1 at PRE followed by metribuzin at 556 g ha −1 30 or 60 DBH and metribuzin at 842 g ha −1 PRE followed by metribuzin at 278 g ha −1 at 30 or 60 DBH. Potato injury was ≤ 8% at all locations, and injury was transient. There were no differences observed between metribuzin rate or application date for individual potato grades or total yield. Reducing the PHI in potato to 30 d would have no effect on yield and would provide a longer period for controlling broadleaf weeds.

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)0.001
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 score0.556
Threshold uncertainty score0.286

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.101
GPT teacher head0.315
Teacher spread0.214 · 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

Citations4
Published2015
Admission routes1
Has abstractyes

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