Beef Symposium: Population data analyses to evaluate trends in animal production systems1
Bibliographic record
Abstract
The Beef Symposium, titled “Population data analyses to evaluate trends in animal production systems,” was 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 Montreal, Quebec, Canada, July 12 to 16, 2009. The symposium was organized with the following objectives: 1) to familiarize society members of techniques and procedures to gather, analyze, and interpret population data; and 2) to demonstrate several applications of population data analyses that may be useful in various animal science disciplines. Population data analysis is a common application of statistical processes in non-animal-science fields for determination of consumer trends, health status, responses to immigration and emigration patterns, and the economic growth of countries. The need for animal scientists and biologists to understand the tools of population data analyses for data gathering, analyses, and interpretation is increasing as budgets shrink and other restraints increase on conducting animal experiments at university centers. Additionally, there are definitive applications for which traditional animal experiments may not be sensitive enough, particularly those with a low frequency of occurrence, such as morbidity and mortality events, and some carcass traits (e.g., incidence of dark cutters).
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 | 0.060 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".