Un système septique modifié pour gerer efficacement les eaux usées de ferme laitière /
Bibliographic record
Abstract
In 2001, the Quebec Ministry of Environment modified its waste management regulation and obliged dairy farms to treat their milk house wastewaters to prevent contamination of water courses. For small dairy farms with fewer than 60 cows, conventional technologies implied an investment of at least $15 000 to comply with the new regulation. The objective of this master's project was therefore to develop a low cost and sustainable technology for the treatment and disposal of milk house wastewaters that would permit on-farm recycling of nutrients and water. With the help of the research results of Urgel Delisle and Ass., the new system was done by modifying existing septic tank systems on two dairy farms with 40-50 cows by installing a sediment and milk fat trap before the septic tank, and building a drained 0.45ha seepage field in a pasture or cropped field, after the septic tank. The modified septic tank system on each farm was monitored during a three year period, which involved checking the system for clogging by digging out sections of sewer pipes after two years of operation; measuring and sampling milk house wastewaters to establish the annual nutrient load, and comparing the water quality in drainage from the seepage field to that of a nearby control field. The milk house wastewaters produced by the farms led to an average nutrient load of 60kg TN/ha/y, 50kg TP/ha/y and 80 kg TK/ha/y. The average volume of wastewater applied to the seepage field, between 16 and 19mm/month, did not saturate the soil as no sign of gleying (reduction of iron oxides) was observed when excavating the sewer pipes. In general, soil pH decreased when milk house wastewater entered the seepage field, while the NH4-N, K and Ca concentrations increased. However, soil salinity was low (<4 dS m -1) on these farms. The soil P concentration was unchanged on one farm, but there was rapid and significant accumulation of P in the 20-60 cm depth of the soil profile on the second farm. The accumulation of milk fat inside the sewer pipes on one farm resulted from the disposal of wasted milk into the septic system, the absence of a water softener and the fact that this fat was not regularly removed from the trap. The milk fat was then flowed into the septic tank harming the correct operation of the system. Drainage water quality was similar from the seepage field of the modified septic tank system as an adjacent control field. The low cost of system modification, about $4 400 Can., and the treatment efficiency achieved meant that the concept is feasible and offers a suitable solution for small dairy farms.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".