Subjective evaluation of community noise in Canada's National Capital Region and its relation to waking levels of salivary biomarkers.
Bibliographic record
Abstract
Some research has suggested an association between long-term exposure to traffic noise and relative risk of cardiovascular disease (Babisch et al., 2005; Willich et al., 2006). It has been assumed that noise may act as a nonspecific stressor. Acute exposure to noise can evoke physiological and behavioral changes reminiscent of a stress response in rodents (Michaud et al., 2003), but it is equivocal that this occurs in humans chronically exposed to traffic noise. This pilot project examined annoyance to community noise and salivary biomarkers known to be influenced by stressor exposure. A face-to-face interview subjectively assessed community noise for 60 residents (30 males, 30 females; mean age 41.3, SD=14.98). Traffic sound levels will be determined and respondents categorized into high (>65 dBA, Leq24) and low (<50 dBA) noise areas. Participants also provided saliva samples upon awakening, 30 min after awakening, and prior to bedtime. Concentrations of salivary biomarkers of alpha-amylase and cortisol were spectrophotometrically determined using commercial enzyme-linked immunosorbant assays. Two-way (high-noise versus low-noise) mixed-model analyses of variance will examine differences in questionnaire and salivary responses. Sex differences will be evaluated with independent t-tests, and polynomial regression analyses will relate salivary biomarker levels to high- or low-noise areas.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".