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Record W2051837245 · doi:10.1614/wt-d-09-00067.1

Effects of Landscape Position, Rainfall, and Tillage on Residual Herbicides

2010· article· en· W2051837245 on OpenAlexaffabout
J. R. Moyer, G. M. Coen, R. A. Dunn, A. M. Smith

Bibliographic record

VenueWeed Technology · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicPesticide and Herbicide Environmental Studies
Canadian institutionsAgriculture Food and Rural DevelopmentAgriculture and Agri-Food Canada
Fundersnot available
KeywordsTillageEnvironmental scienceAgronomyConventional tillageAtrazineNo-till farmingSoil waterSoil sciencePesticideBiologySoil fertility

Abstract

fetched live from OpenAlex

The effect of soil properties and weather on herbicide persistence and injury to following crops were studied at a site near Lethbridge, Alberta, Canada, with undulating topography that included no-tillage and conventional tillage systems on adjacent fields. Soil pH ranged from 5.2 (lower slope no-tillage) to 7.8 (upper slope conventional tillage) and soil organic matter content ranged from 2.3% (upper slope conventional tillage) to 4.4% (lower slope no-tillage). During the years when the experiments were conducted rainfall ranged from < 50% of normal to > 150% of normal. During dry years atrazine and metsulfuron severely injured wheat and lentil crops, seeded 1 yr after herbicide application, on upper slope locations. The most severe injury occurred on the upper slope conventional tillage location. In years with high rainfall, no crop injury occurred 1 yr after atrazine and metsulfuron application on either upper or lower slope locations in both tillage systems. Imazamox plus imazethapyr caused almost 100% injury in the lower slope position in the no-tillage system (pH 5.2) in the driest year. Following-crop injury due to the imidazolinone herbicides decreased with increasing rainfall and increasing soil pH. The most severe injury to following crops seemed to occur when herbicide dissipation was dependent on microbial activity and rainfall was below normal.

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 score0.656
Threshold uncertainty score0.655

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.002
GPT teacher head0.187
Teacher spread0.185 · 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

Citations12
Published2010
Admission routes2
Has abstractyes

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