MétaCan
Menu
Back to cohort
Record W7087066513 · doi:10.5539/jas.v17n11p53

Interaction of Pyroxasulfone and Encapsulated Saflufenacil Applied Preemergence for Control of Waterhemp in Corn

2025· article· en· W7087066513 on OpenAlexvenueaboutno aff

Bibliographic record

VenueJournal of Agricultural Science · 2025
Typearticle
Languageen
FieldEngineering
TopicGas Sensing Nanomaterials and Sensors
Canadian institutionsnot available
Fundersnot available
KeywordsWeed controlBiomass (ecology)Field cornWeedYield (engineering)

Abstract

fetched live from OpenAlex

Waterhemp is a problematic weed in corn production that is rapidly evolving multiple herbicide resistance; 5-way resistance to herbicide groups 2, 5, 9, 14 and 27 has been confirmed in multiple counties across southern Ontario. The introduction of a new premix herbicide, pyroxasulfone plus encapsulated saflufenacil, provides growers with an additional option to control this challenging weed. In 2022 three field trails were conducted at three locations in southwestern Ontario to assess corn injury and yield and waterhemp control, density, and biomass, as well as the interaction between pyroxasulfone and saflufenacil applied preemergence in corn. Herbicide treatments consisted of pyroxasulfone (90, 120, and 150 g ai ha-1), encapsulated saflufenacil (56, 75, and 95 g ai ha-1), pyroxasulfone plus encapsulated saflufenacil (146, 195, and 245 g ai ha-1), and an industry standard, S-metolachlor/atrazine/mesotrione/bicyclopyrone (2026 g ai ha-1). The co-application of both active ingredients improved waterhemp control and resulted in biomass reduction compared to saflufenacil applied alone; waterhemp density and corn yield were similar for pyroxasulfone plus encapsulated saflufenacil or each active ingredient applied alone. The interaction between pyroxasulfone and encapsulated saflufenacil was additive for the majority of assessments; however, there were some parameters where there was an antagonistic or synergistic response.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.044
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.006
GPT teacher head0.207
Teacher spread0.201 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Agricultural ScienceSame topicGas Sensing Nanomaterials and SensorsFrench-language works237,207