Bibliographic record
Abstract
This paper examines the developments that have occurred in South Dakota's swine industry and offers insights into its future prospects. Data, trends, and literature related to this market were gathered in an effort to fill in specific gaps about South Dakota's market. Nationally and in South Dakota there are fewer producers raising hogs. The exit of relatively small producers from the industry reduced a seasonal spike of farrowings during the March-May quarter in recent years. A growing trend of inshipments, where feeder pigs are brought into South Dakota, finished, and marketed, has partially offset the reduction in farrowings. An analysis of a longstanding indicator of supply, farrowing intentions, reveals that the closer intentions reported were more accurate than the distant intentions and that the overall accuracy is impressive. The general price level for market hogs at Sioux Falls follows a similar pattern to U.S. prices. A seasonal trend exists at Sioux Falls, with prices higher from May through August. Analysis of location basis, the difference between the CME Lean Hog Index and the cash price at Sioux Falls, with prices higher from May through August. Analysis of location basis, the difference between the CME Lean Hog Index and the cash price at Sioux Falls, also reveals a seasonal trend and substantial variablity across different months. Knowledge of basis is necessary when comparing different markets and when determining the effectiveness of risk management tools.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".