Handbook of Nutritional Biochemistry: Genomics, Metabolomics and Food Supply
Bibliographic record
Abstract
Preface Nutritional Factors & Osteoporosis Prevention Regulation of Phosphate Transport in Epithelia Methods for the Extraction of Metabolites from Plant Tissues Application of 'Omics' Technologies for Improvement of Meat Quality Role of Chaperones in Dystrophic & Senescent Skeletal Muscle Fibres Amino Acid Composition of Some Aquaculture Fauna Resources in Nigeria The Biochemistry of Isoelectric Processing & Nutritional Quality of Proteins & Lipids Recovered with This Technique Nutritional Biochemistry of Curcumin (diferuloylmethane) & a Review of its Biological Actions on Articular Chondrocytes Effect of Creatine Applied as Food Supplement on Human Metabolism Production, Properties & Stability of Chicken Meat Protein Hydrolsate Powder a-Galactosidase: A Food & Feed Enzyme Foods of Plant Origin as Source of Nitric Oxide Production Inhibitors Application of Plant-Derived Food Lectins in Proteoglycomics & Immunomodulation Biochemical & Proteomic Profiling of Key Metabolic Enzymes in Aging Skeletal Muscle Inhibitory Mechanism of Longer Chain Fatty Acids on Mammalian DNA Polymerase B Activity Endorhizal Fungi Associated with Vascular Plants on Truelove Lowland, Devon Island, Nunavut, Canadian High Arctic Involvement of the Mitochondrial ATP-Sensitive Potassium Channel in the Beneficial Effects of Fasting on the Ischemic-Reperfused Rat Heart Application of Artificial Neural Network for Predicting Physicochemical Properties of Pressure-Induced Gel from Industrial Milk Whey Proteins Index.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.055 | 0.056 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".