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Pulse Phytonutrients: Nutritional and Medicinal Importance

2016· article· en· W2559391363 on OpenAlexvenueno aff
Jagdish Singh, Rajni Kanaujia, Narendra Pratap Singh

Bibliographic record

VenueJournal of Pharmacy and Nutrition Sciences · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPhytase and its Applications
Canadian institutionsnot available
Fundersnot available
KeywordsPhytic acidAntioxidantFood sciencePrebioticResistant starchHealth benefitsVitaminVitamin CBiologyDietary fibreChemistryStarchBiochemistryBiotechnologyTraditional medicineMedicine

Abstract

fetched live from OpenAlex

Pulses are important food crops which offer significant nutritional and health advantages due to their high protein content and a unique nutritional profile, i.e., low fat source of digestible protein, dietary fibre, complex carbohydrates, resistant starch and a number of essential vitamins, especially, the B-group vitamin B9 (folate). In addition to these vitamins and minerals contributing to a healthy diet, pulses contain a number of non-nutritive bioactive substances including enzyme inhibitors, lectins, saponins, phytates, phenolic compounds and oligosaccharides. The latter contributes beyond basic nutritional value and is particularly helpful in the fight against non-communicable diseases often associated with diet transitions and rising incomes. Phytic acid exhibits antioxidant activity and protects DNA damage, phenolic compounds have antioxidant and other important physiological and biological properties, and galacto-oligosaccharides may elicit prebiotic activity. Research findings on different phytochemicals in pulse seeds and their role in preventing the lifestyle diseases has been discussed. Encouraging awareness of the nutritional value of pulses can help consumers adopt healthier diets and also could be an important dietary factor in improving longevity.

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 score0.693
Threshold uncertainty score0.295

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 communication0.0000.001
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 head0.041
GPT teacher head0.308
Teacher spread0.267 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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