<scp>F</scp> ourier Transform Infrared Spectroscopy in Peptide and Protein Analysis
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
Abstract Infrared (IR) spectroscopy is one of the two forms of vibrational spectroscopy, the other being Raman spectroscopy. IR spectroscopy measures absorptions of vibrating molecules and yields information about molecular structures and structural interactions. The development of computerized Fourier transform infrared (FTIR) techniques has opened up new dimensions in biological IR spectroscopy owing to the increase in achievable signal‐to‐noise ratios, wavenumber accuracy, and data aquisition rates, and the ability to perform measurements with strongly absorbing samples. High‐quality FTIR spectra can be obtained with relative ease and rapidly with very small amounts of sample in a variety of environments. Measurements of proteins in aqueous solution are almost routine now, and can be performed under equilibrium and nonequilibrium conditions. There are many IR absorption bands characteristic of peptide groups and amino acid side‐chain groups from which information on protein structures can be obtained. The information provided by FTIR spectroscopy may be a global one or highly specific for a single vibrating chemical group. In some cases, the usefulness of the method is limited by difficulties in extracting the structural information contained in the IR absorption bands.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | medium |
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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