Bibliographic record
Abstract
Environmental issues have expanded to the forefront of livestock production systems. Air and water quality, human health concerns, and pathogens associated with manure are important issues being examined in swine production systems. In addition, methodologies to reduce or eliminate these issues have become important avenues of scientific research. These topics were the focus of the Swine Symposium on Environmental Concerns Based on Swine Production held at the joint annual meeting of the American Society of Animal Science, American Dairy Science Association, and the Canadian Society of Animal Science in Montréal, Québec, Canada, July 12 to 16, 2009. An introduction to the symposium, provided by D. J. Meisinger, executive director of the US Pork Center of Excellence (USPCE) located in Ames, Iowa, summarized 2 invitational workshops designed to develop research and extension needs in air and water quality (Meisinger, 2009). Discussions among experts at these workshops, hosted by the USPCE and the Environmental Committee of the National Pork Board (Des Moines, IA), resulted in the identification of producer materials that should be developed based on available research, a recommended list for development of producer educational and informational materials based on available research, identification of research efforts needed to fill gaps in information, and a recommended priority list for identified research needs (USPCE, 2009).
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".