Manipulation of protein fingerprints during on-column fluorescent labeling: Protein fingerprinting of sixStaphylococcus species by capillary electrophoresis
Bibliographic record
Abstract
Bacterial proteomes were analyzed by use of electrophoretically mediated microanalysis (EMMA) and field-enhanced stacking. A water-soluble protein fraction was injected onto a capillary. Next, a fluorogenic reagent was injected and allowed to react with the protein mixture, producing fluorescent products that were separated by submicellar capillary electrophoresis and detected by laser-induced fluorescence. By use of a low-ionic strength sample buffer and a brief electrophoretic step, slow moving anionic proteins were stacked at the reagent-sample interface and were preferentially labeled. By reversing the order of sample injection and labeling reagent, fast moving cationic proteins were preferentially labeled. By adjustment of the sample buffer pH, proteins with different isoelectric points were selectively labeled. Electrophoresis fingerprints were generated for the water-soluble protein fraction from six Staphylococcus species. The protein patterns produced were species-specific and were used to construct a phylogenetic tree.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".