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Record W2345463667 · doi:10.1093/molbev/msw077

A Lesson from Wheat Evolution: Wild and Landraces Genetic Diversity the Key to Improving the Nutritional Value of Our Spaghetti Dish

2016· letter· en· W2345463667 on OpenAlexaboutno aff
Joseph Caspermeyer

Bibliographic record

VenueMolecular Biology and Evolution · 2016
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNutrition, Genetics, and Disease
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyPolyunsaturated fatty acidArachidonic acidDiseaseBiotechnologyGenetic diversityFatty acidPhysiologyGeneticsFood scienceBiochemistryInternal medicinePopulationEnvironmental health

Abstract

fetched live from OpenAlex

heart disease, colon cancer, and many other inflammationrelated conditions. Treating individuals according to whether they carry 0, 1, or 2 copies of the insertion, and their influence on fatty acid metabolites, can be an important consideration for precision medicine and nutrition. The insertion mutation may be favored in populations subsisting primarily on vegetarian diets and possibly populations having limited access to diets rich in polyunsaturated fats, especially fatty fish. Very interestingly, the deletion of the same sequence might have been adaptive in populations which are based on marine diet, such as the Greenlandic Inuit. The authors will follow up the study with additional worldwide populations to better understand the mutations and these genes as a genetic marker for disease risk. “With little animal food in the diet, the long chain polyunsaturated fatty acids must be made metabolically from plant PUFA precursors. The physiological demand for arachidonic acid, as well as omega-3 EPA and DHA, in vegetarians is likely to have favored genetics that support efficient synthesis of these key metabolites,” said Brenna and Kothapalli in a joint comment. “Changes in the dietary omega-6 to omega3 balance may contribute to the increase in chronic disease seen in some developing countries.”

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.548
Threshold uncertainty score0.760

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 science0.0000.001
Research integrity0.0010.000
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 head0.006
GPT teacher head0.219
Teacher spread0.213 · 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 designNot applicable
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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