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Record W2892064019 · doi:10.1080/09064710.2018.1514419

Characterisation of agro-morphological traits of<i>corchorus</i>accessions

2018· article· en· W2892064019 on OpenAlexaff
Sweetbird Phindile Dube, D. Marais, Sydney Mavengahama, Corlina Margaretha Van Jaarsveld, Abe Shegro Gerrano

Bibliographic record

VenueActa Agriculturae Scandinavica Section B - Soil & Plant Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSeed and Plant Biochemistry
Canadian institutionsScience North
FundersNational Research FoundationAgricultural Research Council
KeywordsGermplasmBiologyHeritabilityCropBiotechnologyHorticultureAgronomyEvolutionary biology

Abstract

fetched live from OpenAlex

The genus Corchorus includes nutrient-rich indigenous leafy vegetables often grown or harvested in the wild in Africa. It has potential to contribute to food security, but there is need to improve it as a crop through plant breeding. However, not much is known about the genetic variability among Corchorus species. The aim of this study was thus to characterise the available germplasm of Corchorus accessions and investigate heritability of their agro-morphological traits. Eleven Corchorus accessions (4 South African and 7 from the World Vegetable Center) were evaluated in a field study at Roodeplaat, South Africa, for their morpho-agronomic traits using a randomised complete block design with three replications across two seasons (2015/2016 and 2016/2017). Data were subjected to ANOVA, principal component analysis and cluster analysis. Broad sense heritability, genetic advance, genetic parameters and correlations were determined among the accessions and traits. The Corchorus accessions showed significant (P < .05) differences in all the quantitative traits evaluated. The data showed significant variability among the studied Corchorus accessions in their agro-morphological traits for exploitation in future breeding programmes that in turn can contribute to the improvement of this crop.

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 score0.582
Threshold uncertainty score0.415

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.016
GPT teacher head0.223
Teacher spread0.207 · 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

Citations11
Published2018
Admission routes1
Has abstractyes

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