Fourier Transform Near Infrared Spectroscopy: A Newly Developed, Non‐Invasive Method To Measure Body Fat
Bibliographic record
Abstract
An FT-NIR technique is reported to provide a fast, accurate and low cost method of determining in-vivo human body fat content. The body fat content of 353 healthy subjects (154 males and 199 females) of various height, weight, and age were measured by FT-NIR and compared to 420 subjects investigated by magnetic resonance imaging (MRI). The procedure involved scanning each subject's upper ear that provided a necessary reflectance surface and proved representative of the subject's subcutaneous fat content. The average FT-NIR spectrum was compared to a reference mixture with known and similar fat content and composition to that of humans. The FT-NIR response was incorporated into an empirical equation using the ratio of subcutaneous to total body fat from MRI data, taking into account the subject's gender, height, weight and age. The results on the two data sets were similar and demonstrated that the FT-NIR technique can be used to obtain a measure of the body fat content of individuals, similar to that using MRI. In addition, the FT-NIR was used to more accurately monitor the fat content of sleep apnea patients.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".