Odour assessment: determining the optimum temperature and time for Tedlar sampling bag pre-conditioning
Bibliographic record
Abstract
Odours present in new Tedlar bags can impact the assessment of emissions from sewer collection systems and wastewater treatment plants. Conditioning protocols are needed to minimise the impact of background materials emissions on the sampling and assessment of odourous emissions. Olfactometry analysis has shown that background odour concentrations for new Tedlar bags can be as high as 130 OU(E)/m(3). Experimental studies were undertaken to investigate the impact of different conditioning temperatures in order to determine the optimum temperature for cleaning new Tedlar bags to a level when no detectable odours were present in the sampling bags via dilution olfactometry. For the purpose of this study, new Tedlar bags were cleaned in a temperature-controlled oven that had a constant filtered air flow-rate. From the analysis of odour and volatile organic compounds (VOCs) concentrations found in new Tedlar bags during the cleaning process, it was observed that odour and VOCs concentrations decreased with time. It was also found that the temperature setting plays a significant role in the cleaning of the Tedlar bags as large concentrations of phenols and acetamide, N,N-dimethyl were found in new Tedlar bags and their concentrations decreased following the temperature pre-conditioning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".