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Record W1983969459 · doi:10.4236/as.2013.46039

Influence of late emerging weeds in glyphosate-resistant corn

2013· article· en· W1983969459 on OpenAlexaffabout
Nader Soltani, Robert E. Nurse, Eric R. Page, Wesley J. Everman, Christy L. Sprague, Peter H. Sikkema

Bibliographic record

VenueAgricultural Sciences · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Guelph
Fundersnot available
KeywordsLambsquartersGlyphosateAgronomyFoxtailWeedBiologyRagweedWeed controlField trialYield (engineering)Biomass (ecology)Chenopodium

Abstract

fetched live from OpenAlex

Fifteen field trials were conducted from 2009 to 2011 in Ontario, Canada and Michigan, USA to determine how long glyphosate-resistant corn needs to be kept weed-free after emergence to prevent yield loss. Data were separated into two environments based on when yield loss first occurred after glyphosate application. In Environment 1 (4/15 sites) yield was not reduced when corn was kept weed-free until the 4-leaf stage. However, in Environment 2 (11/15 sites) there was no yield loss when corn was kept weed-free up to the 2-leaf stage. The most prominent weeds were velvetleaf, redroot pigweed, common ragweed, common lambsquarters and foxtail species. While later emerging weeds did not necessarily impact corn yield, weeds emerging after the 2- and 4-leaf corn stage likely produced seed that was added to the soil seed bank. Weeds emerging after 6-, 8-, and 10-leaf corn growth stages were small (low biomass/seedlings) and most likely did not reach reproductive maturity. Based on this research, corn must be maintained weed-free up to the 4-leaf stage. Any weeds emerging after that did not influence corn yield.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.100
Threshold uncertainty score0.198

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.211
Teacher spread0.200 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations7
Published2013
Admission routes2
Has abstractyes

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