From Farm to Table: How This Little Piggy Was Dragged Through the Market
Bibliographic record
Abstract
DECEMBER 9, 2003, a Holstein cow raised in Alberta, Canada arrived at Vern's Moses Lake Meats ("Vern's") in Washington for slaughter.'Unbeknownst to the slaughtering plant employees, and the rest of America, the cow was infected with Bovine Spongiform Encephalopathy ("BSE"), more commonly known as "Mad Cow Disease." 2 Even more troubling is how Vern's discovered the infected animal.According to Dave Louthan, Vern's then-slaughterer, the cow was not caught by routine inspection, "but by 'a fluke."' 3 Louthan asserts that the cow he killed was not a "downer" cow, 4 although the * Class of 2006; B.A., Ithaca College, 2001.Thank you to Elisa Odabashian with the Consumers Union for inspiring this topic and to Professor Josh Davis and my classmates in Legal Scholarship for their valuable input.Thank you also to Michelle Tschumper, my editor, whose hard work and down to earth attitude guided me through this process.The views expressed in this Comment are the Author's alone and do not necessarily represent those of the Consumers Union.1. Jason R. Odeshoo, No Brainer?The USDA's Regulatory Response to the Discovery of "Mad Cow" Disease in the United States, 16 STAN.L. & POL'Y REv.277, 297 (2005); see also Donald G. McNeil, Jr., Official Tells of Investigation into Mad Cow Discrepancies,
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.053 | 0.012 |
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".