Evaluating Price Impacts for Stated Attributes of Western Canadian Feeder Calves Marketed via Online Auction
Bibliographic record
Abstract
This research provides insight on the value of attributes for feeder calves sold via electronic auction in Western Canada from 2016 to 2020 during the months of August to December. Results from this research can provide producers with valuable information to market their feeder calves more effectively. By identifying attributes buyers value and attributes that are less desired and associated with price discounts producers can make more informed marketing and management decisions. \nLot description details and sales results for 4,866 feeder calf lots (3,235 steer and 1,631 heifer) representing 505,074 head were analyzed using a hedonic pricing model with OLS regression to estimate the price impacts of 17 attributes. The model variables considered lot (gender, number of head, province of origin), genetic (hide colour, frame size), management (weight, weight uniformity, flesh, implant use and weaning status), marketing (age verification, VBP+ mention, EU eligibility) and market structure (marketing week, days to delivery, expected fed price) characteristics. Steer and heifer lots were estimated separately, with a pooled model and five annual models for each sex. \nTraditional feeder calf attributes - weight, lot size and weight uniformity - showed consistently statistically significant results. Marketing attributes ‒ VBP+ and EU Eligibility ‒ only estimated price premiums in the last two years of the steer model. From 2016 to 2020 the percentage of lots mentioning VBP+ increased from 3% to 15% and the percentage of lots noting EU eligibility increased from 0% to 8%. Lack of significance for value-added marketing attributes may be related to a lack of third-party verification for claims made on lot listing reports. The percent of lots mentioning age verification declined 12% from 2016 to 2020 and the premium declined from 2017 to 2019 for steer lots. In all models Charolais-influenced calves received premiums while steer lots with mixed colours were discounted compared to black-hided lots. It can be concluded from the results that feeder calves marketed in larger, more uniform lots with implant status disclosed receive higher prices in the western Canadian online auction market. It is important for sellers to provide all information for a lot of calves when marketing online as buyers value information on attributes when making purchasing decisions.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".