<scp>F</scp> ourier Transform Infrared Spectroscopy in Peptide and Protein Analysis
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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How this classification was reachedexpand
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.572 | 0.506 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".