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Record W2512198768 · doi:10.1094/cchem-04-16-0124-fi

Effects of Cultivar, Growing Location, and Year on Physicochemical and Cooking Characteristics of Dry Beans (<i>Phaseolus vulgaris</i>)

2016· article· en· W2512198768 on OpenAlexaffabout
Ning Wang, Anfu Hou, J. L. S. Santos, Lisa Maximiuk

Bibliographic record

VenueCereal Chemistry · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant pathogens and resistance mechanisms
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsCultivarPhaseolusPhytic acidStarchChemistryDry beanAgronomyDry weightHorticultureFood scienceBiology

Abstract

fetched live from OpenAlex

The effects of cultivar, growing location, and year on physicochemical and cooking characteristics of beans ( Phaseolus vulgaris ) were investigated, and the relationship between these characteristics was determined. Twenty dry bean cultivars and breeding lines were grown at two different locations for two consecutive years (2013 and 2014) in southern Manitoba, Canada. Results indicated that cultivar, growing location, and year had significant effects on seed weight, water hydration capacity, and cooking time of beans. Significant cultivar, location, and year variations in protein, starch, and phytic acid contents in beans were observed. Most of the traits were also significantly affected by the interactions of cultivar × location, cultivar × year, and location × year. Seed weight was negatively correlated with crude protein and ash contents but positively correlated with starch content. Cooking time was negatively correlated with protein, ash, and phytic acid contents but positively correlated with firmness. Phytic acid content in beans was positively correlated with ash content. Knowledge gained from this study will be useful to bean breeders in selecting parental lines for crossing and cultivar development in efforts to improve the quality of beans.

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 score0.028
Threshold uncertainty score0.128

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 head0.004
GPT teacher head0.175
Teacher spread0.171 · 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

Citations27
Published2016
Admission routes2
Has abstractyes

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