Bibliographic record
Abstract
An economic model of pork, swine, and related markets examines effects of hypothetical classical swine fever (CSF) outbreaks in the United States. Equations determine deviations in endogenous variables from observed supply and demand values. The analysis assumes 11 million US hogs are destroyed. Live swine and pork exports are stopped during the outbreak, with full recovery. Pork demand by US consumers is assumed to fall by 1 % during the outbreaks, with a gradual recovery. Hog growers adjust expectations of future prices on the basis of current market conditions. One potential CSF outbreak reflects losses in the hog population skewed towards grower and finisher swine, while another outbreak has stronger effects on breeding inventory and the pig crop. The largest effects occur in pork and swine. Effects on other sectors are small. Over 20 quarters, the pork industry returns lose $4 billion. Losses for hogs, including the value of animals destroyed, range from $2.6 billion to $4.1 billion. An assumption of unchanged hog-grower expectations for returns is compared to that when expectations adjust. Unchanged expectations alter the pattern of slaughter and prices because breeding inventory falls less, thus more hogs are available for sale after Quarter 7.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.142 | 0.035 |
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".