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Record W2051777396 · doi:10.5539/jas.v4n1p31

Effect of Plant Density on Morphologic Characteristics Related to Lodging and Yield Components in Different Rice Varieties (Oriza Sativa L.)

2011· article· en· W2051777396 on OpenAlexvenueno aff
Yadi Reza, Morteza Siavoshi, Mobasser Hamidreza, Salman Dastan, Alireza Nasiri

Bibliographic record

VenueJournal of Agricultural Science · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRice Cultivation and Yield Improvement
Canadian institutionsnot available
Fundersnot available
KeywordsPanicleTiller (botany)Randomized block designAgronomyMathematicsBiology

Abstract

fetched live from OpenAlex

In order to study the effects of plant density on agronomical and morphologic characteristics which are related to the lodging in rice varieties, an experiment was carried out in 2008 in split plot in randomized complete block design based on 4 replications in Iran. In this experiment, five rice varieties of Tarom (Local, Hashemi, Dilamani, Langhrodi and Sangh) were chosen as main factors and three plant density levels (40, 80, and 120 bushes per m²) as sub-factors. The results showed that Langhrodi Tarom had minimum total number of spikelets and number of hollow spikelets per panicle and minimum tiller per bush and number of panicles per m² had seen sequentially in Local Tarom and Sangh Tarom. The shortest length of first, second, and third inter-nodes, longest diagonal of the third inter-node and minimum plant height obtained for Langhrodi Tarom. Number of node and length of first, second and third inter-node and diagonal of the forth inter-node and Stem tension to lodging in third and forth inter-node decreased as plant density increased. Minimum lodging index of the third and forth inter-node came out at 80 bushes per m². Interaction effects in plant density had significant effects on all parameters except panicle number per m².

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.830
Threshold uncertainty score0.148

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 communication0.0000.000
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 head0.037
GPT teacher head0.220
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

Citations10
Published2011
Admission routes1
Has abstractyes

Explore more

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