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Biochemistry of Sugars

2016· article· en· W2566908252 on OpenAlexvenueno aff
Helena Jenzer

Bibliographic record

VenueCanadian Journal of Clinical Nutrition · 2016
Typearticle
Languageen
FieldMedicine
TopicDiet, Metabolism, and Disease
Canadian institutionsnot available
Fundersnot available
KeywordsBiochemistryChemistryComputational biologyBiology

Abstract

fetched live from OpenAlex

A sugar is a polyalcohol with at least one of them oxidized to either an aldehyde or a ketone. As a result, aldoses and ketoses are distinguished. The most simple sugar is glycerinaldehyde with a chain length of 3 carbons. Elongation of the chain leads from trioses to tetroses, pentoses, hexoses, heptoses, octoses, nonoses. Monosaccharides such as glucose, fructose, galactose, either alone or in combination with proteins or lipids, can build branched or unbranched carbohydrate chains of tremendous diversity. Carbon 5 sugars such as ribose and desoxyribose are ingredients for the RNA and DNA backbone structure. C6 sugars such as glucose, fructose, or galactose are key substrates for energy production, and for anabolic biosynthesis of structural elements such as cell walls. Their “fingerprint” is specific for cell lines and serves for instance as analyte to determine blood groups. Linkage to proteins and lipids mediates interactions among cells. Raw sugar is extracted from various plants such as sugar cane or sugar beet. Refining is the following process to remove the molasses. Cristallization yields the white sugar. Eating carbohydrates in turn will hydrolyse glycosidic bonds as far as physiological enzymes are available in human metabolism. The degree of hydroysis is also dependent on processing of meals. Risotto for example is slowly hydrolysed during the cooking process and the profile of carbohydrate fragments and free glucose depends on this procedure. Glucose liberated from various carbohydrates leads to different degrees of biologically availability, which is expressed as the glycemic index. Keywords: Biochemistry, Glycemic Index, Monosaccharides, Polysaccharides, Sugars

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.330
Threshold uncertainty score0.421

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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 head0.047
GPT teacher head0.368
Teacher spread0.321 · 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 designObservational
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

Citations0
Published2016
Admission routes1
Has abstractyes

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