Evaluation of mathematical models to predict methane emissions from ruminants under different dietary mitigation strategies
Bibliographic record
Abstract
GGAA history, purpose, aims and audienceThe 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. GGAA2019The GGAA2019 focused on the theme "Science supporting Practices" and happened for the first time in Latin America.Following on from the six previous GGAA conferences, presented the latest research on the measurement, modelling, mitigation of greenhouse gases, while also seeking to discuss adaptation efforts and the impact of these advances for farmers, managers and policy makers.At the GGAA2019 conference, held in Iguassu Falls, Brazil, almost 200 delegates from 39 countries gathered to participate in a program featuring 11 invited keynote speakers, 47 offered presentations and 111 poster presentations.The conference was organised into four sessions covering: Technical advances: from genomics to precision agriculture, that addressed aspects related to Measuring and Modelling GHG; Farm level low carbon initiatives, which addressed Mitigation and Adaptation strategies to provide more Resilient production systems; Regional low carbon initiatives, which considered the Landscape, Regional management and National Commitments; International low carbon initiatives, that presented examples from IPCC, NDCs, FAO, World Bank.Delegates were offered the opportunity to publish their research in this peer-reviewed Special Edition of Animal, Cambridge University Press, with all abstracts published in conference proceedings.Volume One of the GGAA2019 Special Edition will include 20 peer-reviewed papers and will be available within the last quarter of 2019.The 'virtual' Volume Two will present the Proceedings… in a digital way, available in the web site.Taken together, these special editions provide the latest summary of the current state of knowledge on policy developments, measurement, modelling, mitigation and adaptation efforts associated with greenhouse gases from animal agriculture. VENUEThe city of Iguassu Falls was selected to host the event for the first time in Latin America thanks to its excellent geographical location within the triborder region of Brazil, Argentina and Paraguay.The city boasts an international airport that connects to the leading international airports in South America and can easily be reached by Latin American participants via bus or car.Besides the facilitated access, Iguassu Falls is further complemented by technical and scientific attractions part of the context of the conference, such as waterfalls, a national park, bioenergy and hydroelectric complex which were included as technical visits.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".