Roadkill Compost: Turn Dead Animals Into Disease-Free Ground Cover
Bibliographic record
Abstract
Several states are now composting the carcasses of animals killed on their roads instead of paying to have them hauled away or burying them by the roadside. Cornell University's Waste Management Institute in Ithaca, New York, has made presentations on the practice in numerous states as well as Canada. Composting animals does not require windrow turning because coarse wood chips used in the process permit air to flow through the pile naturally. The pile quickly reaches 104 degrees Fahrenheit, hot enough to break down muscle tissue and kill enough bacteria for Class B use. The Cornell process suggests 12 months for converting carcasses into material that can be used in low-public-contact settings such as highway rights of way. Some states do not use compost derived from deer, elk, moose, or antelope, for fear of infection from prions. Other agencies say such fears are overblown. A link to the Cornell group's Web site is provided. http://cwmi.css.cornell.edu/tirc.htm.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".