Deodorization of pig manure by organic bed biofiltration
Bibliographic record
Abstract
The growth of pig industry has caused a greater problem of undesirable odours, particularly in and around production buildings, storage areas and when the pig manure is spread.By measuring the intensity and duration of odour emissions, it was established that the sources of odour in Québec were at 20% for buildings, 10% for storage, 5% for recovery and 65% for spreading.Increasingly stringent standards and heightened public awareness regarding environmental issues, has led to an increase in research on various treatment methods used in different countries.Among manure treatment options, organic bed biofiltration represents a very promising technique for the deodorization and treatment of pig manure.Research and development work to optimize the BIOSOR TM -Manure, a biofiltration process for simultaneously treatment of liquid and gaseous effluents on pig farms, have been realized on the site of a piggery (Île d'Orléans, Québec, Canada) using a 560 m 3 biofiltration system.The results obtained show that the BIOSOR TM -Manure process is an efficient, simple and performing technology bringing a global solution to odours pig manure problems.Actually, in reducing over 95% the polluting load from the gas of the pig farm (NH 3 , H 2 S), the BIOSOR TM -Manure process eliminates over 80% the odour intensity coming from the production installations, the storage, the transportation and the spreading of the manure.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".