MétaCan
Menu
Back to cohort
Record W2919990151 · doi:10.2298/abs181106012b

The response of weedy sunflower (Helianthus annuus L.) to nicosulfuron: An examination of vegetative parameters and acetolactate synthase activity

2019· article· en· W2919990151 on OpenAlexaff
Drаgаnа Bоžić, Marija Sarić‐Krsmanović, Ana Matković, Filip Vranješ, Snežana Jarić, Sаvа Vrbničаnin

Bibliographic record

VenueArchives of Biological Sciences · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsInstitute for Biological Sciences
FundersMinistarstvo Prosvete, Nauke i Tehnološkog Razvoja
KeywordsAcetolactate synthaseHelianthus annuusSunflowerBiologyWeedPopulationAgronomyWeed controlBotanyGeneticsGene

Abstract

fetched live from OpenAlex

Genetic and morphological interpopulation variability of weed species is often responsible for variable responses to herbicides. As weedy sunflower, an invasive form of Helianthus annuus L., possesses high morphological and genetic variability, very different responses of its populations to herbicides can be expected. This species is one of the dominant weed species in row crops, including maize, in many European countries in which nicosulfuron is intensively used for weed control. There are little available data about the response of this sunflower form to nicosulfuron or of the interpopulation variability of its response to other herbicides. The responses of three weedy sunflower populations to nicosulfuron were studied in field dose-response experiments, and acetolactate synthase (ALS) enzyme activity at different herbicide concentrations was determined in vitro. Interpopulation variability in the response to nicosulfuron was confirmed. Populations WS2 and WS3 were more that 20-fold and 30-fold less susceptible to nicosulfuron, respectively, than population WS1, based on fresh weight, whereas the differences were not so prominent based on other parameters, including plant height, leaf area and ALS activity, and ranged from 2 to 12-fold.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.932
Threshold uncertainty score0.392

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.026
GPT teacher head0.259
Teacher spread0.233 · 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

Citations8
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueArchives of Biological SciencesSame topicWeed Control and Herbicide ApplicationsFrench-language works237,207