Bibliographic record
Abstract
This paper focuses on the problem of numeracy when writing regulations, specifically how to describe a threshold for crowding of pigs during transport, considering transported pigs range in body mass from 5 to 500 kg. When scientific findings provide the basis for regulation in the public interest, those findings must be communicated in a consistent way to regulators and policymaking bodies. Numeracy is the ability to understand, reason with, and apply appropriate numerical concepts to real-world questions. Scientific understanding is almost always based on rational understanding of numerical information, numeracy. The threshold of administrative offenses is often a numerical description. Commercial livestock transporters have an interest in loading livestock compartments to the maximum to achieve the largest payload allowed by axle weight laws, as is the case in all bulk commodity transport. Maximizing payload minimizes costs and environmental hazards of fuel exhaust and can benefit the public with lower pork prices, but has a serious animal welfare risk. Livestock production academics, veterinarians, and animal welfare activists have been working for decades to determine the level of livestock crowding in transport containers that would be appropriate for regulatory enforcement. The scientific discourse has been plagued by a lack of numerical standardization when describing results of trials and forming recommendations. Exceeding specific numerical thresholds is the core to implementing enforcement actions. This paper examines the communication and other barriers that have prevented emergence of a consensus on this question and provides a direction toward resolution. Further confirmation of effects of crowding livestock in transit is needed. This paper suggests that articulating an enforceable standard in pig transport is possible. In inspection for compliance, discovering the LP50 (lethal pressure—50) for slaughter-weight pigs is an initial global benchmark goal. The LP50 is the loading floor pressure in a commercial transport compartment, under field conditions, that would result in the death of at least one pig in the group 50% of the time.
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 | 0.024 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.043 |
| Scholarly communication | 0.014 | 0.027 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.014 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".