Bibliographic record
Abstract
The present paper provides the results from two nation-wide telephone surveys conducted in Canada on a representative sample of 5,232 individuals, 15 years of age and older. The goals of this study were to gauge Canadians' annoyance towards environmental noise, identify the source of noise that is viewed as most annoying and quantify annoyance toward this principal noise source according to internationally accepted specifications. The first survey revealed that nearly 8% of Canadians in this age group were either very or extremely bothered, disturbed or annoyed by noise in general and traffic noise was identified as being the most annoying source. A follow-up survey was conducted to further assess Canadians' annoyance towards traffic noise using both a five-item verbal scale and a ten-point numerical scale. It was shown that 6.7% of respondents indicated they were either very or extremely annoyed by traffic noise on the verbal scale. On the numerical scale, where 10 was equivalent to "extremely annoyed" and 0 was equivalent to "not at all annoyed", 5.0% and 9.1% of respondents rated traffic noise as 8 and above and 7 and above, respectively. The national margin of error for these findings is plus or minus 1.9 percentage points, 19 times out of 20. The results are consistent with an approximate value of 7% for the percentage of Canadians, in the age group studied, highly annoyed by road traffic noise (i.e. about 1.8 million people). We found that age, education level and community size had a statistically significant association with noise annoyance ratings in general and annoyance specifically attributed to traffic noise. The use of the International Organization for Standardization/Technical Specification (ISO/TS)-15666 questions for assessing noise annoyance makes it possible to compare our results to other national surveys that have used the same questions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".