Vertical-Flow Constructed Wetlands in Cooperating with Oxidation Ponds for High Concentrated COD and BOD Pig-Slaughterhouse Wastewater Treatment System at Suphanburi-Provincial Municipality
Bibliographic record
Abstract
Oxidation Pond (OP) as engineering tool is generally used for treating the pig slaughterhouse wastewater which normally contains high concentration ofd COD and BOD in effluent. Unfortunately, it cannot reduce the organic substance (blood, hairs, grease, meats, solid dunks and some contaminants) from pig slaughtering areas under the 2-consecutive oxidation ponds by producing the minimum values of COD 151.92 mg/L, BOD 79.14 mg/L, coliform bacteria 2.6 x 10-5 MPN/100mL, and fecal bacteria 1.5 x 10-5 MPN/100mL but all of them above the standard values. After treating the effluent by VFCW-Typha from the 2-consecutive oxidation ponds, the results found COD 90.92 mg/L, BOD 31.67 mg/L, coliform bacteria 1.5 x 10-4 MPN/100 mL and fecal bacteria 2.0 x 10-3 MPN/100 mL which were almost above the standard values. It is noted that the modification of 2-m consecutive ponds to 4-m consecutive ponds in cooperating the prolongation of VFCW-Typha length instead of 30 meters to 40-50 meters, width 3-5 meters, and still keeping 1-m depth would be enough to support the pig-slaughterhouse wastewater treatment system. Summarily speaking, the experimental results have been brought to say that the Oxidation Pond as the engineering tool could not be applicable in slaughterhouse wastewater treatment that containing high concentration COD and BOD from slaughtering and dissecting activities.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".