Circadian Rhythm Factor in Relation to the Analysis And Interpretation of Infrared Thermography Results in the Arctic (Review)
Bibliographic record
Abstract
The number of studies explaining the role of environmental factors in research using infrared thermography in the Arctic is still limited. This article is focused on circadian rhythms, which can influence both the analysis and interpretation of infrared thermography results in the Arctic. Literature published between 1981 and 2019 was selected with the help of PubMed search engine by means of a systematic search by the keyword infrared thermography using the PRISMA system. Having studied the abstracts of relevant open access articles, we selected a total of 81 papers: 40 American, 15 Russian, 11 Canadian, 6 Swedish, 4 Danish, 3 Finnish, and 2 Norwegian. Having assessed the materials and methods against the area of application (medicine and dentistry), we found 12 articles in full compliance with the selection criteria. In conclusion, taking into account different day lengths and light intensities in the Arctic, we point out three circadian rhythm mediators affecting its physiological activity. These are as follows: light of sufficient intensity, suprachiasmatic nuclei and neurotransmitters. Their influence is often reduced in the summer and is linked with changes in skin temperature. Therefore, it is important for researchers to consider time, season, and sleep patterns of the subjects during the selection process in order to obtain accurate temperature measurements using infrared thermography. For citation: Nasution A.I., Pankov M.N., Kir’yanov A.B. Circadian Rhythm Factor in the Analysis and Interpretation of Infrared Thermography Results in the Arctic (Review). Journal of Medical and Biological Research, 2021, vol. 9, no. 4, pp. 444–453. DOI: 10.37482/2687-1491-Z082
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".