Living with Livestock: Dealing with Pig Waste in the Philippines
Bibliographic record
Abstract
As livestock production increases worldwide, livestock waste is becoming a serious environmental hazard. In some cases, the damages have been spectacular and even tragic. In June 1995, the artificial waste lagoon at a hog farm in North Carolina burst. The sudden release of nearly 100 million litres of hog urine and feces polluted neighbouring communities and killed millions of fish in nearby rivers (Worldwatch, March/April, 2001). In 2000, drinking water contaminated by livestock waste led to several deaths in the small Canadian town of Walkerton. In other cases, livestock waste causes continuous and pervasive damage to people's health and the environment. This study investigated a number of solutions and highlighted those that could mitigate the problem - given adequate support from policy makers. To find out the actual situation on the ground, the study looked at both the on-site and off-site impacts of hog farming in Majayjay. 176 households were surveyed, including 82 households of swine raisers and 94 households that live within a 20-meter radius of a hog farm. An additional comparative survey was made of 50 households that were not affected by air pollution from the 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 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) | 0.005 | 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".