The development of experimental procedures for the evaluation of additives to attenuate manure odour, and the impact of these additives on workers, animals and the environment
Bibliographic record
Abstract
The objective of this project was to develop a laboratory research protocol to evaluate the effect of additives on manure odour and physico-chemical characteristics, and establish conditions that are representative of those found in farm storage structures (temperature, solids content, pH, ventilation above the manure surface, storage period). The results suggested that system configuration might have an impact on additive effect. An open system should be used when it is recommended that additives be applied in the animal diet or the gutters. Additionally, the surface/depth ratio of the gutter should be respected, since it will impact on the relative importance of the aerobic layer and on ammonia volatilization. On the other hand, a closed system should be used when the additive is applied to the manure storage tank, especially if the tank has a cover. Odour analysis still requires fundamental research to establish reliable procedures and protocols, especially in the area sample collection and dilution levels required to decrease H2S concentration to safe levels for the panellists. Odour analysis should also be conducted in triplicate, because of the possible large experimental error due to dilution, the human factors, and also instrumental error.
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".