Environmental footprint of livestock and life balance on the ranch
Bibliographic record
Abstract
North America is a role model when it comes to reducing the environmental footprint of livestock production. Dr. Frank Mitloehner is a professor with the University of California Davis. He told delegates at the recent Saskatchewan Swine Symposium the public needs to recognize the contributions of livestock agriculture to the reduction of greenhouse gas emissions. He said the most pressing need is to get the countries that are not efficient in producing animal sourced food to change, and that objective can be met while, at the same time, minimizing the carbon footprint.Shelby Corey wears many hats. She is a wife and mother. She is a rancher, a rural municipal councillor, and a development officer with 4-H Saskatchewan. Life is hectic. Shelby will share how balancing the farm, kids, career, and everything else can be overwhelming but there is a way to find balance. She will share some of the things she has learned to avoid burnout.See omnystudio.com/listener for privacy information.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.027 | 0.001 |
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 teacher head, 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".