On-chip\nEnrichment and Analysis of Peptide Subsets\nUsing a Maleimide-functionalized Fluorous Affinity Biochip and Nanostructure\nInitiator Mass Spectrometry
Bibliographic record
Abstract
A new\nnanostructure initiator mass spectrometry (NIMS) methodology\nis presented that uses the strategy of fluorous-phase immobilization\nand capture by a maleimide-functionalized affinity tag to selectively\nenrich peptide subsets containing cysteine residues. This surface-based\napproach allows complex protein digests to be analyzed. The proposed\nplatform makes use of a chemically unmodified porous silicon (pSi)-based\nNIMS chip. Unlike matrix-assisted laser desorption ionization (MALDI)\nmass spectrometry (MS), the approach described in this paper does\nnot require analytes to be incorporated or cocrystallized with an\ninitiator. The mass spectra generated by the approach in this work\nare characterized by low background noise and, therefore, high analyte\ndetection sensitivity. Experiments were also conducted that show the\npotential the approach described in this work has for generating simplified\nmass spectra for MS/MS analyses.
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How this classification was reachedexpand
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.040 | 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 teacher head, 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".