Bibliographic record
Abstract
Knowledge of the existence of amino acids dates back over a century in many cases, as does knowledge of their existence in proteins (see ). When amino acids were discovered, their identity was established by isolating and purifying the individual compounds and obtaining elemental analyses After the advent of paper chromatography, this technique was used with a variety of different solvents to identify elution characteristics and demonstrate the purity of isolated compounds. Amino acids were located by the use of a reagent that produced a color with the compound. The most common reagent used for locating amino acids is ninhydrin, which produces a purple color with amino acids, a pink or yellor color with amino acids, and various intermediate colors with compounds containing an amino group and a sulfonic acid, and so on. It also reacts with small peptides such as glutathione. The techniques of paper chromatography were applied to the separation of mixtures of amino acids, such as the components of a protein after hydrolysis, and then to the separation of free amino acids in physiologic fluids and tissues. It was extended by the use of two-dimensional chromatography, in which a different solvent was used in each direction Later, electrophoresis was employed as one of the separating techniques
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.024 |
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".