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Record W4200240879 · doi:10.5539/jas.v14n1p85

Proximate and Mineral Nutrition of Common Bean Genotypes as Influenced by Harvesting Time

2021· article· en· W4200240879 on OpenAlexvenueno aff
Onkgolotse G. Moatshe-Mashiqa, Patrick K. Mashiqa, Odireleng O. Molosiwa

Bibliographic record

VenueJournal of Agricultural Science · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural pest management studies
Canadian institutionsnot available
Fundersnot available
KeywordsProximateLegumeZincNutrientGrowing seasonBiologyAnimal scienceHorticultureAgronomyMineralBotanyChemistryFood scienceEcology

Abstract

fetched live from OpenAlex

Common bean is the most consumed legume by humans and hence significant for global food security and nutritive value mainly iron (Fe) and zinc (Zn). The objective of the study was to evaluate the effect of harvesting time and genotypes on proximate and mineral composition of common bean. Two field experiments were carried out in Sebele (24o33′S, 25o54′E, 994 m above sea level) horticultural fields during summer 2018/2019. Treatments included two common bean genotypes DAB 564 (white) or 520 (red) and three harvesting stages as the early, mid and late season harvest. In both seasons, genotype and harvesting time significantly (P < 0.05) influenced proximate, macro and micro mineral composition of common bean seeds. Generally genotypes studied had a sufficient amount of proximate and mineral content with crude protein, iron (Fe) and zinc (Zn) content as the most abundant minerals at a range between 20-22%, 33-101 mg g-1 and 41-45 mg g-1, respectively across harvesting times. Genotype DAB 564 (white coloured) significantly (P < 0.05) produced seeds with the highest concentration of proximate and mineral contents especially zinc (45 mg g-1) and iron (70 mg g-1) content irrespective of season. Harvesting common bean seed early or mid-stage produced high seed concentration of proximate, micro and macro mineral content across genotype or season.

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 score0.887
Threshold uncertainty score0.280

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.008
GPT teacher head0.216
Teacher spread0.208 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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