Bibliographic record
Abstract
Measurement of odour emissions from swine operations is a difficult task. Olfactometers are currently the most accepted method for odour measurement. However, obtaining downwind odour samples that are representative to what is actually “felt ” by those in the field is almost impractical. The goal of this study was to evaluate a potentially more satisfactory method of evaluating odour directly in the field. This method was developed by St. Croix Sensory Inc. (Stillwater, MN) to use trained human odour assessors (Nasal Rangers) to quantify odour intensity according to n-butanol reference scales. The specific objective of this study was to establish a relationship between odour intensity assessed by the Nasal Ranger technique and odour concentration measured with olfactometers. Four swine production sites were selected for this study, two located in Southern Manitoba and two in Central Alberta. A total of 154 samples were collected in 10 L Tedlar bags from the four sites between June and October, 2001. While these samples were being collected, odour intensity of the ambient air was assessed (the field odour intensity) by two or more Nasal Rangers using the 8-point n-butanol reference scale. Odour intensity of bagged samples was also measured in the laboratory by the Nasal Rangers. Odour concentrations of bagged samples were
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.600 | 0.507 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".