Survey on foaming deep-pit swine manure
Bibliographic record
Abstract
Abstract. Foaming on manure surfaces in deep-pit barns is not a completely new challenge, but starting in 2009 foaming incidents significantly increased. In the past five years, 30 to 40 flash fires and/or explosions, believed to be related to the presence of foaming in deep-pits, have been reported in the Midwest U.S. and Canada. In this study, a survey (electronic and hard copy) was developed and distributed to swine producers throughout the Midwest to document the prevalence of foaming on swine farms with deep-pits and determine if any correlations exist between foam formation and facilities and management practices. The survey results were collected from September 2012 to May 2013. Total of 225 producers (80.0% IA, 8.9% IL, 4.9% MO, 3.1% MN, 2.2% MI, 0.9% IN) responded to the survey. There were 1388 rooms (65.5% grow-finish and 34.5% wean-to-finish) and 1334 deep-pits. Foam was present in 322 pits out of 1,334 pits (24.1%). Out of 225 producers, 132 producers (58.7%) had at least one foaming pit. Total of 13 producers, 9.8% of the producers who had at least one foaming pit, had an explosion or a flash fire. Foam was a concern mainly in summer and fall. Only 37% of the producers checked their pits frequently (once a week). Solids content of the manure and unpleasant smell in drinking water were found to be significantly correlated with the presence of foam.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".