Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it