Production, Management, and Environment Symposium: Environmental footprint of livestock production – Greenhouse gas emissions and climate change1
Bibliographic record
Abstract
The 2015 Production, Management, and Environment symposium titled “Environmental Footprint of Livestock Production – Greenhouse Gas Emissions and Climate Change” was held at the Joint Annual Meeting of the American Society of Animal Science and American Dairy Science Association at the Rosen Shingle Creek Resort in Orlando, FL, on July 15, 2015. The symposium was organized by the Production, Management, and Environment program committee composed of Scott Radcliffe, Purdue University (committee chair); Trevor DeVries, University of Guelph; Al Rotz, USDA-ARS, University Park, PA; Don Ely, University of Kentucky; Phil Cardoso, University of Illinois; and N. Andy Cole, USDA-ARS, Bushland, TX (session chair). The purpose of the program was to provide up-to-date information regarding the impact of livestock production on greenhouse gas emissions and climate change, potential mitigation strategies, and methodologies to use in research. The symposium comprised 5 invited presentations, 2 of which were expanded to the manuscripts in this journal issue. Each of the presentations is subsequently briefly discussed.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 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".