Bibliographic record
Abstract
This paper will demonstrate the differences found in odour test results, when odour sampling is performed at the same sources by two different consultants. By examining two case studies, this paper will highlight that the difference between the results can be significant. Both studies are based on odour sampling programs determining the odour removal efficiency of odour control units installed at two different facilities: a pet food facility and an oil/grease recycling facility. The first study is based on odour measurements at the inlet and outlet of the unit installed by Applied Plasma Physics AS at the pet food facility. Odour assessments were performed by two separate consultants at the same time. The second study is based on testing of the odour removal effectiveness of two units: a scrubber and a biofilter at an oil/grease recycling facility. During this study two odour sampling programs were performed by two consultants at different times, but under the same process conditions. This paper will show how varying results can play a role in choosing the adequate odour control technologies. The final results suggest that although, an odour control unit may appear to be insufficient, it actually is successful at removing the odours.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".