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Record W2164142698 · doi:10.5539/jas.v4n12p134

Influences of Different Nitrogen Levels on Competition between Spring Wheat (Triticum aestivum L.) and Wild Mustard (Sinapis arvensis L.)

2012· article· en· W2164142698 on OpenAlexvenueno aff
Pejman Behdarvand, Ganesh Shridhar Chinchanikar, Kondiram Dhumal

Bibliographic record

VenueJournal of Agricultural Science · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgronomic Practices and Intercropping Systems
Canadian institutionsnot available
Fundersnot available
KeywordsBrassicaRandomized block designAgronomyInterspecific competitionSinapisNitrogenWeedCompetition (biology)Mustard seedField experimentBiologyYield (engineering)Grain yieldHorticultureChemistryBotany

Abstract

fetched live from OpenAlex

A field experiment was carried out to investigate the effects of different nitrogen levels on interspecific competition between wild mustard and spring wheat. The experiment was laid out in randomized complete block design under a split plot arrangement, with three replications. The experiment comprised of three nitrogen levels (90, 150 and 210 kg ha-1) assigned to main plots and four wild mustard densities such as 0, 5, 10 and 15 plants m-2 kept in sub-plots. The results revealed that yield, yield components and nitrogen use efficiency of wheat were decreased with increasing wild mustard density. The density of 5, 10 and 15 wild mustard plants m-2 reduced the grain yield of wheat by 21.4, 32.2 and 40.2% respectively as compared to control. Increasing nitrogen level increased the grain yield of wheat in weed free plots, while in the presence of wild mustard, increasing nitrogen level led to increase the competitive ability of wild mustard and increased the yield losses of wheat. The density of 15 wild mustard plants m-2 had decreased the grain yield of wheat by 31.6, 34.4 and 53.3 % under 90, 150 and 210 kg N ha-1 respectively.

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 score0.642
Threshold uncertainty score0.199

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 studies0.0000.000
Scholarly communication0.0000.001
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 head0.036
GPT teacher head0.251
Teacher spread0.215 · 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

Citations5
Published2012
Admission routes1
Has abstractyes

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