Bibliographic record
Abstract
Abstract Infrared (IR) spectroscopy provides information relating to the vibration of covalent bonds within molecules. The wavelength of light absorbed by a vibrating bond depends upon the atoms in the bond, the type of bond, the type of vibration and inter‐ and intramolecular interactions. For complex samples such as human tissues an IR spectrum therefore provides a direct indication of sample biochemistry. With the correct choice of sampling methodology (usually an IR microscope) information on the biochemical nature of disease states can be obtained from tissue samples, which can often be useful diagnostically. Variations in spectral signatures arising from nucleic acids, proteins and lipids can provide important information in a number of disease states, including Alzheimer's disease (AD), breast cancer and skin cancer. The information obtained by IR spectroscopy is difficult to obtain with many other instrumental techniques. For example the signal‐to‐noise ratio obtained by IR microscopy is far superior to that seen with Raman methods, allowing more sophisticated data processing and so more information to be extracted. Furthermore, many species of interest cannot be studied in situ with other techniques. Nucleic acids are a case in point. Powerful techniques such as nuclear magnetic resonance spectroscopy provide no information concerning this material, while Raman techniques only provide information relating to individual nucleotides. In contrast, DNA and RNA give rise to IR signatures that provide information relating to nucleic acid content/structure. To appreciate fully the information contained in the complex spectra obtained from human tissues and cells, a unique combination of expertise in spectroscopy, biochemistry and anatomy/histology is required. This combination allows the investigator to avoid potential artefacts due to incorrect sampling and spatial variations in sample composition and to attribute the major absorptions present in spectra to individual biochemical species. However, spectral interpretation is often a highly subjective process, a fact that is made worse when one considers that many thousands of spectra are often acquired from a single tissue section. The application of pattern recognition techniques to IR data removes this subjectivity and allows realistic processing of these large data sets. In addition, many new methods are being developed which allow presentation of these complex data sets in a form readily interpreted by the nonexpert.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".