Bibliographic record
Abstract
It is interesting to note that since the first edition of this book, the most significant welfare concerns for cattle during transport have remained unchanged. These concerns include the transport of unfit (sick, emaciated, debilitated) cattle, overloading - particularly in lightweight and young animals - and excessive transport distances with long periods between food, water and rest. There is also concern about marketing through auctions, and more information is needed on transportation durations experienced by cattle (usually of poor condition or quality) that are sold and resold through the auction markets. Trips of over 30 hours should be avoided if possible because death losses increase sharply. Ambient temperatures below -15°C or above 30°C are detrimental and space allowances (using an allometric coefficient, the k value) lower than 0.015 and greater than 0.035 are associated with greater losses. Cattle that lose 10% of their bodyweight during transport have a greater likelihood of dying, becoming non-ambulatory or lame. A study of heath records from many feedlots indicated that mortality was 1.3% and sickness 4.9%. Truck drivers with more years of experience had fewer compromised animals. Feeder cattle destined to feedlots were twice as likely to die during transport compared with fattened cattle. To provide incentives to reduce losses, there needs to be economic accountability throughout the supply chain for dead, non-ambulatory cattle bruises and dark cutting meat. There also needs to be economic accountability for failure to precondition and vaccinate beef weaners before they leave the ranch of origin.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.008 |
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".