MétaCan
Menu
Back to cohort
Record W7099987058

Weed Management in Kidney Bean with Tank Mixes of S-Metolachlor, Imazethapyr

2014· article· en· W7099987058 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEthnobotanical and Medicinal Plants Studies
Canadian institutionsnot available
Fundersnot available
KeywordsLambsquartersWeed controlCropWeedField experiment
DOInot available

Abstract

fetched live from OpenAlex

Field studies were conducted in various locations in Ontario during 2011 to 2013 to evaluate s-metolachlor, imazethapyr and linuron applied preemergence (PRE) alone and in tankmix combi-nation for the control of troublesome weeds in kidney bean. S-metolachlor, imazethapyr, linuron, s-metolachlor + imazethapyr, s-metolachlor + linuron and s-metolachlor + imazethapyr + linuron applied PRE at rates evaluated caused 3 % or less injury in kidney bean. S-metolachlor provided 87 %-91 % control of redroot pigweed, 46 %- 55 % control of common lambsquarters, and 96 %- 97% control of green foxtail. Imazethapyr provided 93 %- 96 % control of redroot pigweed, 96 %- 99% control of lambsquarters and 86 %- 93 % control of green foxtail. Linuron provided 82 %- 98% control of lambsquarters, 82 %- 99 % control of redroot pigweed and 55 %- 85 % control of green foxtail. The tank mixes of s-metolachlor plus imazethapyr, s-metolachlor plus linuron, and s-me-tolachlor plus imazethapyr plus linuron provided 92 %- 100 % control of lambsquarters, redroot pigweed and green foxtail. Generally, kidney bean yields reflected the level of weed control. Based on these results, tank mixes of s-metolachlor plus imazethapyr, s-metolachlor plus linuron, and s-metolachlor plus imazethapyr plus linuron all provide an adequate margin of crop safety and ex-

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.735
Threshold uncertainty score0.207

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 head0.012
GPT teacher head0.210
Teacher spread0.197 · 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 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

Citations0
Published2014
Admission routes1
Has abstractyes

Explore more

Same topicEthnobotanical and Medicinal Plants StudiesFrench-language works237,207