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Record W2212815861 · doi:10.4141/cjps-2014-188

Screening of different nitrogen rates and intra-row spacing effects on yield and yield components of safflower (<i>Carthamus tinctorius</i> L.) under microclimate conditions, Iğdır Plain, Turkey

2014· article· en· W2212815861 on OpenAlexvenueno aff
Tamer Eryi̇ği̇t, Rifat Akış, Ali Rahmi Kaya

Bibliographic record

VenueCanadian Journal of Plant Science · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSunflower and Safflower Cultivation
Canadian institutionsnot available
Fundersnot available
KeywordsCarthamusRandomized block designYield (engineering)NitrogenMicroclimateInteractionAgronomyMathematicsCrop yieldHorticultureAnimal scienceBiologyChemistryEcologyMaterials science

Abstract

fetched live from OpenAlex

Eryiğit, T., Akiş, R. and Kaya, A. R. 2015. Screening of different nitrogen rates and intra-row spacing effects on yield and yield components of safflower (Carthamus tinctorius L.) under microclimate conditions, Iğdır Plain, Turkey. Can. J. Plant Sci. 95: 141–147. The yield of safflower (Carthamus tinctorius L.) is affected by many factors, among which nitrogen fertilization and plant density are significant. This study was conducted as a split plot in a randomized complete block design arrangement with four replications, during the successive seasons 2011 and 2012, to determine the effects of nitrogen application rate and intra-row spacing on the growth, yield and yield components of safflower under the ecological conditions of the Iğdır Plain, Turkey. The main plots were three intra-row spacing (IRS 1 , IRS 2 and IRS 3 ) and subplots were four nitrogen rates (N 1 , N 2 , N 3 and N 4 ). Intra-row spacing had significant effects on all parameters except plant height and seed oil content. There were significant effects of fertilizer rate on all parameters except seed oil content. The interaction of nitrogen rate and years had significant effects on seed yield. Correlations showed significant negative results between 1000-seed weight and seed oil content (−0.217). However, there was a high seed yield in 2011 compared with 2012. Among intra-rows, IRS 2 and IRS 3 gave greater yield compared with IRS 1 , while N 3 gave a higher yield than other N rates (0, 100, 150, 200 kg ha −1 ) in both years of the study, especially 2011.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.593
Threshold uncertainty score0.272

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 studies0.0000.000
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 head0.021
GPT teacher head0.204
Teacher spread0.183 · 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 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

Citations3
Published2014
Admission routes1
Has abstractyes

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