Educating Producers to Systematically Evaluate the Cows they are Culling
Bibliographic record
Abstract
Our research will assess the effectiveness of an effort to educate Ontario dairy producers to systematically evaluate the cows they plan to cull. Using a number of health and welfare-related criteria, our team aims to advance decision-making of producers regarding whether culled cows are fit for transport. Our goal is to educate dairy producers to eliminate the shipping of unfit cows, involve their veterinarians in the decision-making process, and to encourage development of standard operating procedures (SOPs) for evaluating cull cows. Currently, Ontario’s dairy industry lacks a systematic evaluation method to aid producers in the decision to remove and transport cows. Moreover, the dairy industry’s proAction initiative requires all farms to have a documented SOP regarding the shipping of cattle. Additionally the veterinary community is interested in being more involved in the SOP development and decision-making process on their client’s farms. This pilot research project will run from January to April 2017, inclusive. Twenty bovine veterinarians have been recruited to each enrol 10 of their dairy clients. Veterinarians will provide their clients with a Pre-survey, Cull Cow Evaluation Forms, an information package, and a Post-survey. The Pre-survey is intended to gage the knowledge of dairy producers regarding culling and shipping cattle and assess their current attitudes and practices. The producers will then be asked to complete an Evaluation Form for every cow they cull from the milking herd during the study period. A Post-survey will then be sent to the producers to assess if their attitudes and practices have changed.
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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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".