Greenhouse Gas and Animal Agriculture Conference: proceedings of the 7th GGAA - august 4th to 8th, Iguassu Falls/Brazil.
Bibliographic record
Abstract
The Greenhouse Gas and Animal Agriculture Conference (GGAA) is the premier international conference summarising the collective state of scientific knowledge on greenhouse gas abatement strategies and production systems adaptation needs for the livestock sector. The gathering features leading scientists and policymakers reviewing the current state of knowledge and presenting significant new developments in policy, measurement, modelling, mitigation and adaptation efforts associated with greenhouse gases from animal agriculture. The Conference takes place every 3 years, moving from continent to continent at each edition. The first conference, GGAA2003, was held in Japan with 200 delegates from 20 countries. Five subsequent GGAA conferences have been convened: GGAA2005, Switzerland; GGAA2007, New Zealand; GGAA2010, Canada, with the biggest in Ireland, GGAA2013, attracting 460 delegates from 41 countries. The GGAA2016 in Australia received more than 300 delegates from 36 countries.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.113 | 0.040 |
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".