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Record W2592147008 · doi:10.2298/abs160212092v

Velvetleaf (Abutilon theophrasti Medik.) productivity in competitive conditions

2016· article· en· W2592147008 on OpenAlexaff
Sаvа Vrbničаnin, Eleonora Onć-Jovanović, Drаgаnа Bоžić, Marija Sarić‐Krsmanović, Danijela Pavlović, Goran Malidža, Snežana Jarić

Bibliographic record

VenueArchives of Biological Sciences · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsInstitute for Biological Sciences
FundersFP7 Research Potential of Convergence RegionsMinistarstvo Prosvete, Nauke i Tehnološkog Razvoja
KeywordsAbutilonWeedBiologyMonocultureAgronomyCompetition (biology)ProductivityInterspecific competitionPopulationWeed controlIntraspecific competitionBotanyEcology

Abstract

fetched live from OpenAlex

Velvetleaf (Abutilon theophrasti Medik.) is an invasive alien species in many countries and one of the major weeds in summer row crops worldwide. Weed-management techniques that reduce weed production need to be investigated to provide new approaches. The first step in this process is the determination of weed productivity in different competitive conditions. Field experiments were conducted in 2006 and 2008 in an experimental field in Padinska Skela to quantify growth and seed production of velvetleaf in maize, as well as in a velvetleaf monoculture. A density of velvetleaf ranging from 1 to 8 plants m-1 was artificially created. In a mixture with maize, velvetleaf was sown in crop rows. The growth of velvetleaf was estimated based on plant height, fresh aboveground biomass and leaf area index (LAI). Velvetleaf fecundity was determined as seed mass plant-1 and seed mass m-2. Differences between years in plant production were very prominent. In general, velvetleaf productivity in maize depended on its density. Intraspecific competition had a major influence on growth and seed production when velvetleaf density was from 4 to 8 plants m-1 in maize rows. This information indicates that environmental conditions and weed density can promote/reduce inter- and intraspecific competition and help in the construction of population dynamics models to predict population density, seed bank and competitiveness of weeds and reduce inputs for weed management.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.854
Threshold uncertainty score0.561

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 studies0.0000.002
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 head0.031
GPT teacher head0.254
Teacher spread0.223 · 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

Citations11
Published2016
Admission routes1
Has abstractyes

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