MétaCan
Menu
Back to cohort
Record W2039260598 · doi:10.1614/ws-07-173.1

Velvetleaf (<i>Abutilon theophrasti</i>) Response to Glyphosate on the Field Edge

2008· article· en· W2039260598 on OpenAlexaff
Robert E. Nurse, Kerry Bosveld, Susan E. Weaver

Bibliographic record

VenueWeed Science · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsGlyphosateAbutilonAgronomyCropBiologyCanopyWeed controlWeedAnnual plantHorticultureBotany

Abstract

fetched live from OpenAlex

Control of weeds growing around field edges to limit seed production is an important component of preventative weed management. POST herbicide rates that are effective on weeds growing within a dense corn or soybean canopy may not be high enough to control weeds at the edge of a field. A study was conducted from 2004 through 2006 to compare velvetleaf growth and fecundity at the edge of the field as opposed to within the crop in response to a range of glyphosate rates. Treatments included position (plot center or edge), time of emergence (VE or V4 crop growth stage) and glyphosate rate (0 to 900 g ae ha −1 ). Without herbicide application, velvetleaf plants grown on the edge flowered earlier, had thicker stems, and produced more seed capsules than plants grown in the center of the plots. At glyphosate application rates of 200 to 900 g ha −1 , the percentage of plants surviving and reproducing was higher on the edge than within the crop. Edge plants treated with 900 g ha −1 of glyphosate produced more seeds than center plants that received no herbicide. Dose–response curves were used to estimate the glyphosate rate that would reduce seed production of surviving plants to 80% of the untreated plants. Plants emerging at the VE stage were estimated to require 300 g ha −1 within the corn or soybean canopy and 668 g ha −1 on the crop edge, whereas plants emerging at the V4 stage would require 0 g ha −1 within the canopy and 280 g ha −1 on the crop edge.

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)0.001
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 score0.939
Threshold uncertainty score0.927

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.023
GPT teacher head0.240
Teacher spread0.217 · 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

Citations1
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueWeed ScienceSame topicWeed Control and Herbicide ApplicationsFrench-language works237,207