Bibliographic record
Abstract
As profit margins become smaller, North American beef producers rely on economies of scale and efficiency of production to remain economically viable. Morbidity and mortality of feedlot cattle adversely affect profitability through decreased performance of sick and dead animals, increased costs associated with treatment, increased cost of feed consumed by animals that die prematurely, and loss of the animal’s purchase price, the latter being the single largest cost in beef production (Jim 2009). In feedlots of the mid-Western United States, total morbidity ranges from 5-11% of animals received and total mortality ranges from 0.57% to 1.07% of animals received (Smith 1998). Musculoskeletal diseases in the feedlot account for between 6% and 11% of morbidity, with chronic musculoskeletal conditions accounting for 40%–60% of cattle sold for salvage slaughter prior to reaching target weight (Edwards 2002). Proper management of these chronically ill and lame cattle represents a major opportunity for minimizing losses. Nonetheless, feedlot injuries are one of the most overlooked and mis-diagnosed conditions in feedlots (Stokka et al 2001). This paper will use the case of a chronically lame yearling steer in a Western Canadian feedlot as a gateway to discuss causes of bovine lameness in feedlots.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".