Transportation conditions for feeder and yearling cattle transported by road to Ontario sales barns or feedlots
Bibliographic record
Abstract
Transportation conditions for feeder and yearling cattle movement within Canada were surveyed and evaluated. The objectives of this research were to survey conditions that feeder cattle are transported under when shipped to feedlots and/or sales barns, and evaluate that data to examine how transportation factors (i.e. floor space, transit time and driver training) are adhered to compared to the current regulations or recommendations. The data collected in the present study along with past research can be used to help validate or refute the current legislation's Health of Animals Act with potential to make further recommendations to the Canadian Food Inspection Agency (CFIA) surrounding cattle transportation practices. Analysis showed the majority of the feeder and yearling cattle included in this study originated and were also delivered to a destination within Ontario. A total of 66% of truck drivers have not completed a certified training course, truckers have on average 18.9 years experience trucking cattle and the floor space provided per animal decreased with each kilometre increase in distance travelled for both short haul and long haul trucks. Visual animal welfare concerns were low; 98.8% of the surveyed cattle had no visual signs (lameness, non-ambulatory, sweating, etc.) of poor welfare as assessed by the researcher or truck driver upon delivery.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".