Factors that influence the price of cattle in livestock auctions: the case of the stockyard of Melipilla (Chile)
Bibliographic record
Abstract
This study aims to weigh the influence of observable traits on live cattle prices, i.e., the percent impact on the final price of various physical characteristics. These characteristics are those that commonly are published in auction catalogues, as is the case of: average lot weight, lot size, age, breed and corporal condition. In addition, the influence of seasonality and business cycles were also explored. Using the data collected from the records of El Tattersall S.A., a hedonic price function was fitted. The results showed that the most influential variables with respect to the price of beef cattle, in decreasing order, are as follows: ‘condition’, ´breed’, ‘quarter of sale’ and ‘year’, which are followed far behind by ‘lot size’ and ‘average weight’. The market pays a premium for Red Friesians, Crossbreds and Herefords and punishes the price of Holstein Friesians. Additionally, higher prices are paid in the last two quarters of the year than in the first two, and the third quarter is preferable to the fourth. Finally, the average lot weight and lot size are variables that have a positive, but small, influence on the price of cattle.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".