Bibliographic record
Abstract
Infrared light (760 nm-1 mm) constitutes approximately 40% of the solar radiation reaching the ground at sea level. Shortest wavelength near-infrared (NIR) photons (NIR or IR-A: 760-1,400 nm) can penetrate the epidermis, dermis, and subcutaneous tissue with numerous biological effects. NIR used to have a bad reputation on the basis of past studies using high-intensity artificial light sources (above the solar IR-A irradiance threshold) at high doses leading to detrimental effects (i.e., upregulation of matrix metalloproteinase-1). However, when looking at the other side of the coin and what we can learn from the sun, NIR intensity matters. Hence, mimicking sunlight NIR intensity (30-35 mW/cm2) will rather trigger beneficial cutaneous effects. It is likely that intensity is more important than the fluence (dose) delivered. Moreover, the law of reciprocity (i.e., the biological effect is directly proportional to the total dose irrespective of intensity) does not always apply when considering tissue response in photobiology. In fact, the biphasic dose curve (Arndt-Schulz curve) of photobiomodulation establishes that if irradiance is lower than the physiological threshold value for a given target, it does not produce beneficial effects, even when -irradiation duration is extended. Also, photo-inhibitory deleterious effects may occur at higher irradiances. Remarkably, the beneficial "sweet spot" in between corresponds to the irradiance of the sun. NIR might even precondition the skin from an evolutionary standpoint as exposure to early morning NIR wavelengths in sunlight may prepare the skin for upcoming mid-day harmful UVR. Consequently, NIR light appears to be the solution, not the problem.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".