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Record W2001741744 · doi:10.3329/jsr.v3i1.6078

Correlation and Path Coefficient Studies for Plant Characters in Aqua Aroids, <i>Colocasia esculenta</i> (L.) Schott

2010· article· en· W2001741744 on OpenAlexaff
KK Paul, MA Bari, SC Debnath

Bibliographic record

VenueJournal of Scientific Research · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicPacific and Southeast Asian Studies
Canadian institutionsAgriculture and Agri-Food Canada
FundersRajshahi UniversityUniversity Grants Commission
KeywordsPetiole (insect anatomy)InflorescenceColocasia esculentaPath coefficientBiologyHorticultureBotanyPositive correlationTendrilYield (engineering)StolonLeaf bladeNegative correlationPath analysis (statistics)MedicineMathematics

Abstract

fetched live from OpenAlex

The nature and extent of correlation and path coefficients of aqua edible aroid taro (Colocasia esculenta L., Panikachu) accessions were studied for plant height, petiole length, petiole breadth, leaf number, leaf length, leaf breadth, leaf area index, inflorescence length, peduncle length, spath length, spath breadth and yield per plant. The yield per plant showed significant and positive phenotypic correlation with petiole length (0.481), leaf length (0.576), leaf breadth (0.918), leaf number (0.620), inflorescence length (0.662), spath length (0.890) and spath breadth (0.992). The residual effect was 0.2205 which indicated that characters studied contributed 78% of yield per plant. At genotypic level, yield per plant expressed positive and significant correlation with plant height (0.560) and leaf number (0.600). The residual effect (0.424) indicated that about 58% yield was contributed by these characters.Keywords: Genotypic correlation; Phenotypic correlation; Path coefficient; Aqua aroid; Panikachu (Colocasia esculenta L Schott.stoloniferous).© 2011 JSR Publications. ISSN: 2070-0237 (Print); 2070-0245 (Online). All rights reserved.doi:10.3329/jsr.v3i1.6078 J. Sci. Res. 3 (1), 169-176 (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)0.017
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.437
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.079
GPT teacher head0.389
Teacher spread0.310 · 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.

Study designQualitative
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
Published2010
Admission routes1
Has abstractyes

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