Detecting chronotype differences associated to latitude: a comparison between Horne--Östberg and Munich Chronotype questionnaires
Bibliographic record
Abstract
BACKGROUND: Chronotype, phase preference to perform activities during a 24-hour day, represents distinct circadian temporal organization of living organisms. Morning and evening types can be identified by questionnaires such as Horne and Östberg (HO) and Munich Chronotype Questionnaire (MCTQ). Environmental factors, such as different light-dark cycles experienced at different latitudes, interact with the organisms' circadian timekeeping system. Therefore, chronotype is expected to vary as a result of different geographical locations. AIM: To identify differences in chronotype distribution in populations of two Brazilian cities, Natal and Sao Paulo, located at different latitudes. SUBJECTS AND METHODS: Two specific questionnaires, the Horne and Östberg Questionnaire (HO) and the Munich Chronotype Questionnaire (MCTQ), were used to identify chronotypes of undergraduate students from São Paulo and Natal. RESULTS: The comparison of the curve distributions of HO and MCTQ scores between both cities allowed one to observe that, while HO curves of São Paulo and Natal overlapped, MCTQ curves showed a clear shift towards eveningness in São Paulo. CONCLUSION: This experiment confirmed results from previous studies that the farther away from the equator, the longer the delay of the sleep phase. It was also concluded that MCTQ is better at detecting this phenomenon.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".