Mathematical Equations for Reducing Water Pollution Problems among Poultry Production Clusters in Nong Khai Province, Thailand
Bibliographic record
Abstract
Nong Khai raise layer chickens in the poultry houses constructed over fish ponds. Layer farmers use chicken manure to feed their fish. However, unbalance between chicken raising and the size of fish pond or number of fish results in low water quality, which affects fish production and the community's public water resource. In this field study, data was collected from layer farms in three poultry production clusters in Nong Khai province in the northeast of Thailand (a total of 90 farms) between April and August 2013. Data collected consisted of observations of dissolved oxygen (DO) values of water in the fish ponds, the number of egg-laying chickens raised above the fish ponds, the number of fish and size of the fish ponds. When all of these four observations were analyzed, mathematical equations for calculating the number of chickens raised, the number of fish per one rai (1600 m 2 ), and the size of pond suitable for the number of chickens and fish were obtained as follows: 1) number of fish/rai = 5796 + 1097(size of pond) (R 2 = 0.71), 2) number of chicken/rai = 513 + 223(size of pond) (R 2 = 0.48) and 3) size of pond = -0.328474 + 0.000262(Fish) + 0.00117(Chicken) (R 2 = 0.40). When pond = desired pond size, fish = number of fish to be raised and chicken = number of chickens to be raised.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".