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Record W2158874224 · doi:10.1614/wt-08-132.1

Tolerance of Foxtail Millet to Combinations of Bromoxynil, Clopyralid, Fluroxypyr, and MCPA

2009· article· en· W2158874224 on OpenAlexaffabout
William E. May, Eric N. Johnson, Dan J. Ulrich, Christopher B. Holzapfel, G. P. Lafond

Bibliographic record

VenueWeed Technology · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsMCPAClopyralidFoxtailBromoxynilAgronomyGlyphosateWeed controlWeedMecopropBiology

Abstract

fetched live from OpenAlex

When solid stands of foxtail millet are cut for swath grazing, the grazing season for cattle is extended and winter feeding costs reduced. The economic success of this practice depends on inexpensive weed control. Eight single- and double-rate herbicide combinations (g ai/ha) were evaluated and compared to a weed-free check: MCPA + bromoxynil (280 + 280 or 560 + 560); MCPA + clopyralid (560 + 100 or 1120 + 200); MCPA + fluroxypyr (562 + 108 or 1124 + 216); and MCPA + clopyralid + fluroxypyr (560 + 100 + 144 or 1120 + 200 + 288). This study was conducted at Indian Head, Saskatchewan (SK) in 2004, 2005, 2006, and 2007 and at Scott, SK, in 2006 and 2007. Crop injury and dry matter yield were measured. Results of the study indicate that crop injury exceeded 20% at only one out of six sites at 7 to 14 d after herbicide treatment. Double-rate MCPA + bromoxynil treatments had higher injury ratings than the weed-free check 7 to 14 d after treatment in all site years. As the growing season progressed, injury ratings tended to decline except at Scott in 2007, where injury ratings at the 21 to 35 d period were numerically greater than the other two rating periods. There were no differences among treatments for crop biomass production. We conclude that all four herbicide combinations at the labeled rate are safe to use on foxtail millet in Saskatchewan, and probably in other areas with similar environmental growing conditions.

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 score0.739
Threshold uncertainty score0.154

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.007
GPT teacher head0.209
Teacher spread0.202 · 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

Citations3
Published2009
Admission routes2
Has abstractyes

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