Bibliographic record
Abstract
An extensive literature review has been conducted to collect and analyze information on technologies and practices in odour measurement and mitigation. The review and analysis are focused on the following nine areas: (1) odour measurement and odour evaluation technologies; (2) odour production and odour release quantification; (3) feed additives and dietary manipulation for odour reduction; (4) manure additives; (5) in-barn manure handling systems; (6) manure storage design and management; (7) biofiltration; (8) dust control; and (9) emerging technologies for odour measurement and control. The suitabilities of odour management technologies to Manitoba are evaluated in terms of cost and climatic conditions. Over 168 odour compounds have been identified in livestock odours. These individual odour compounds may be measured with analytical instrument such as GC or GC/MS, but there is no established correlation between the individual odour compounds and the human perception of odour. The most reliable way of measuring odour is using the human olfactory sense (nose). Dynamic-dilution olfactometers with trained human assessors are considered to be the industry standard for measuring odour concentration. However, there are considerable inconsistencies in the design and operation of olfactometers. A national standard should be developed, or existing
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.687 | 0.515 |
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".