A Case Study of Cattle Prices in Nicaragua
Bibliographic record
Abstract
Latin America has become the leading region for beef and poultry exports worldwide (FAO, 2020a). We conduct a case study of cattle prices in Nicaragua, the leading meat producing country in Central America.1 Using data on futures on feeder cattle prices from the Chicago Mercantile Exchange Group (CME) supplemented with a two-year data on 2,520 sales transactions from 99 auctions from the Nicaraguan Cattle Auction (NCA), this study conducts a hedonic price analysis for cattle auctioned in Nicaragua. In particular, the study empirically identifies factors affecting price differentials for cattle and examines their correlation with the futures market. Our results show that weight, lot size, and class are among statistically significant factors impacting cattle auction prices while their correlation with the futures market is significant for six out of the eight futures variables corresponding to the contract months at the CME. The results of the study help Nicaraguan cattle buyers and sellers understand information from the futures market to predict price differences and reduce price risk and uncertainty.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".