MétaCan
Menu
Back to cohort
Record W2166361611 · doi:10.1614/wt-08-122.1

Development of a Laboratory Bioassay and Effect of Soil Properties on Sulfentrazone Phytotoxicity in Soil

2009· article· en· W2166361611 on OpenAlexaffabout
Anna M. Szmigielski, J.J. Schoenau, Eric N. Johnson, F.A. Holm, Ken L. Sapsford, Juxin Liu

Bibliographic record

VenueWeed Technology · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Saskatchewan
Fundersnot available
KeywordsPhytotoxicityBioassayAgronomySugar beetWeedShootSoil waterChemistryBiologyEnvironmental scienceSoil scienceEcology

Abstract

fetched live from OpenAlex

Sulfentrazone is a phenyl triazolinone herbicide used for control of certain broadleaf and grass weed species. Sulfentrazone persists in soil and has residual activity beyond the season of application. A laboratory bioassay was developed for the detection of sulfentrazone in soil using root and shoot response of several crops. Shoot length inhibition of sugar beet was found to be the most sensitive and reproducible parameter for measurement of soil-incorporated sulfentrazone. The sugar beet bioassay was then used to examine the effect of soil properties on sulfentrazone phytotoxicity using 10 different Canadian prairie soils. Concentrations corresponding to 50% inhibition (I50values) were obtained from the dose–response curves constructed for the soils. Sulfentrazone phytotoxicity was strongly correlated to the percentage organic carbon (P = 0.01) and also to percentage clay content (P = 0.05), whereas correlation with soil pH was nonsignificant (P = 0.21). Because sulfentrazone phytotoxicity was found to be soil dependent, the efficacy of sulfentrazone for weed control and sulfentrazone potential carryover injury will vary with soil type in the Canadian prairies.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.008
GPT teacher head0.198
Teacher spread0.190 · 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 source (direct Gemma or distilled Codex), 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

Citations54
Published2009
Admission routes2
Has abstractyes

Explore more

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