CASE STUDY: Searching for the Ultimate Cow: The Economic Value of Residual Feed Intake at Bull Sales
Bibliographic record
Abstract
Cow-calf producers seek to reduce costs and increase profits by selecting bulls that produce more efficient offspring. Organizers of formal bull auctions usually produce catalogs for potential buyers that advertise bull performance measures and genetic characteristics, including EPD and simple performance measures (SPM). Buyers use this information to make decisions regarding bull purchases based on heritable bull traits. Residual feed intake (RFI) is a relatively new SPM of feed efficiency. The Midland Bull Test company (Columbus, MT) measures RFI in addition to other SPM during bull performance testing. The Midland Bull Test company records individual animal feed intake by using GrowSafe (Airdrie, Alberta, Canada) technology. Residual feed intake for each bull is calculated as the difference between actual and expected feed intake. The Midland Bull Test company included RFI along with EPD and other SPM in its 2008 and 2009 sale catalogs. A linear hedonic price model was used to quantify RFI values with various bull performance measures from the Midland Bull Test sale catalogs and associated bull sale prices. Analyses indicate that buyers were willing to pay more for bulls that were RFI efficient (P<0.01). Although other performance measures (e.g., BW gain, birth weight, and age) were valued more highly (P<0.01) by bull purchasers, an RFI SPM could eventually be valued to the extent that an RFI EPD might be developed.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".