MétaCan
Menu
Back to cohort

Growth and yield of rice as influenced by different planting techniques

2017· article· en· W2764194010 on OpenAlexaff
Surajit Kundu, D. Mandal, Rakesh Yonzone, Bimal Das

Bibliographic record

VenueJournal of Hill Agriculture · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRice Cultivation and Yield Improvement
Canadian institutionsExtendicare (Canada)
Fundersnot available
KeywordsSowingYield (engineering)Forensic scienceVeterinary medicineBiologyAgronomyBiotechnologyMedicineToxicologyMaterials science

Abstract

fetched live from OpenAlex

A field experiment was conducted at the farm of Uttar Dinajpur Krishi Vigyan Kendra, Uttar Banga Krishi Viswavidyalaya, Chopra, Uttar Dinapur, West Bengal, India to study the effect of different planting techniques on growth and yield of rice (cv. Koushalya). The results obtained for two years on tillering pattern depicted that maximum tiller number (715.7 m-2) was obtained in system of rice intensification (SRI) method at 70 days after sowing (DAS). Rice crop grown with SRI technique matures at least 4–7 days earlier than other planting methods. SRI technique produced maximum plant height (152.9 cm), moderate number of chaffy grains per panicle (25.9), maximum number of in-effective tillers (43.2 m-2), whereas, double planting technique produced least plant height (133.1 cm) as well as least number of ineffective tillers (16.0 m-2). SRI technique also out yielded all other treatments with respect to yield components, grain yield (5.6 t ha-1) and straw yield (7.6 t ha-1). However, double transplanting method recorded highest harvest Index (HI), while, it was least with SRI method. Considering all the factors, SRI method proved to be the best method as compared to other transplanting methods under study.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.362
Threshold uncertainty score0.162

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 head0.011
GPT teacher head0.221
Teacher spread0.210 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Hill AgricultureSame topicRice Cultivation and Yield ImprovementFrench-language works237,207