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Record W1984578968 · doi:10.1520/jai102770

Spray Solution <i>pH</i> and Glyphosate Activity

2010· article· en· W1984578968 on OpenAlexaboutno aff
Donald Penner, Jan Michael

Bibliographic record

VenueJournal of ASTM International · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Surface Properties and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsGlyphosateChemistryWeedSulfuric acidHydrochloric acidLambsquartersHorticultureBotanyNuclear chemistryAgronomyBiologyChenopodiumInorganic chemistry

Abstract

fetched live from OpenAlex

Abstract It was hypothesized 110 years ago that substances that dissolve in lipids pass more easily into cells than those that dissolve in water. Thus, weak acids such as glyphosate should more readily pass through the non-polar plant cuticle in the non-dissociated, more non-polar form. The objective of this study was to test this hypothesis in a series of studies in the greenhouse involving three weed species. The pH of the glyphosate spray solution was varied between 1.5 and 6. The glyphosate activity on giant foxtail, common lambsquarters, and velvetleaf (pH) was not affected by pH values between 1.5 and 6 when adjusted with hydrochloric acid (HCl). If diammonium sulfate, was also present, the results were similar; however, the glyphosate activity on velvetleaf was much greater. If the pH was lowered with sulfuric acid, H2SO4, an increase in the glyphosate activity on velvetleaf became evident as the pH was lowered from 6.0 to 1.5. This was attributed to the water conditioning effect of the SO4= in hard water. With tank-mixing glyphosate with 1 % NT®NT (N TANK) is a product of Adjuvants Plus, Kingsville, Ontario, Canada N9Y 255., a proprietary blend of surfactants and monocarbamide, which lowered the pH to 2.0, the water conditioning benefit of NT was decreased on velvetleaf as the pH was raised from 2.0 to 7.0. In summary, the hypothesis was disproved with respect to glyphosate.

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 score0.865
Threshold uncertainty score0.305

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 head0.011
GPT teacher head0.207
Teacher spread0.196 · 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

Citations2
Published2010
Admission routes1
Has abstractyes

Explore more

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