Ambient Odour Assessment Similarities and Differences Between Different Techniques
Bibliographic record
Abstract
This paper compares the results for ambient odour concentrations measured by three different techniques, one of which is a new developed technology; Scentroid SM110. This new instrument was developed recently in Canada for assessing ambient odour concentrations. The results obtained by this instrument were compared with two other techniques presently used for assessing ambient odours: the first technique which combines source odour testing with dispersion modelling to predict off-site odour concentration and a second technique; direct ambient odour measurement which includes collection of odour samples at the sensitive receptors with olfactometry analysis. In recent years in Canada, direct ambient odour measurements are more common and are usually combined with source testing and dispersion modelling analysis. A previous study that I carried out determined a correlation between off- site odour concentrations estimated by dispersion modelling analysis which were based on the measured odour emission rates at each potential odour sources and the direct ambient measurements using odour sampling at the sensitive receptors with olfactometry analysis. Since this time however, a new instrument - the Scentroid SM110 was developed for assessing ambient odours, and as a continuation of my previous study, this paper shows the results between the measurements obtained by the new instrument compared to traditional ambient sampling and olfactometry analysis. In addition to the comparison of these two techniques, a third technique is also contrasted to this study; the Nasal Ranger technique, which in a previous study showcased some deviancies.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".