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Injury and Yield Effects on Crops Grown in CGA 152005-Treated Soil<sup>1</sup>

2001· article· en· W1886214437 on OpenAlexaff
John O’Sullivan, Robert J. Thomas

Bibliographic record

VenueWeed Technology · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicPesticide and Herbicide Environmental Studies
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsSativumAgronomyPepperPhytotoxicityLycopersiconCrop rotationChemistryHorticultureCropBiology

Abstract

fetched live from OpenAlex

The effect of CGA 152005 residues in the soil on six crops grown in rotation with field corn was investigated over a 2-yr period. CGA 152005 at 10, 15, 20, and 30 g ai/ha was applied postemergence (POST) to corn in 1994. CGA 152005 at 15 and 30 g ai/ha, atrazine at 1,000 g ai/ha, CGA 152005 at 15 g plus atrazine at 500 g ai/ha, and CGA 152005 at 30 g plus atrazine at 1,000 g ai/ha were applied POST to corn in 1995. Soybean, pea, cabbage, tomato, pepper, and potato were planted each spring, 1 yr after herbicide application. Cabbage exhibited injury and yield reductions that increased with increasing application rate, and pepper exhibited slight injury at the highest rate and yield reduction in 1995. Cabbage yields were reduced by CGA 152005 plus atrazine, and tomato yields were reduced by CGA 152005 and CGA 152005 plus atrazine in 1996. Yields of other crops were not affected in either year.Nomenclature: CGA 152005 (proposed common name, prosulfuron), 1-(4-methoxy-6-methyl-triazin-2-yl)-3-[2-(3,3,3-trifluoropropyl)-phenylsulfonyl]-urea; cabbage, Brassica oleracea L. var. capitata L.: corn, Zea mays L.; pea, Pisum sativum L.; pepper, Capsicum annuum L.; potato, Solanum tuberosum L.; soybean, Glycine max (L.) Merr.; tomato, Lycopersicon esculentum Mill.Additional index words: Crop injury, herbicide carryover, soil pH, soil temperature.Abbreviations: OM, organic matter; POST, postemergence; SU, sulfonylurea.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score0.992

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)0.0010.001

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 head0.006
GPT teacher head0.204
Teacher spread0.198 · 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

Citations5
Published2001
Admission routes1
Has abstractyes

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