Innovative Research for Organic 3.0 - Proceedings of the Scientific Track
Bibliographic record
Abstract
The future challenges in food production and consumption appear clear: \n-\tFeed 9 to 11 billion people in the next 30 to 80 years with enough, affordable and healthy food. \n-\tProtect the environment (e.g. soils, water, air, biodiversity and landscapes) whilst increasingly under pressure to achieve greater levels of intensification. \n-\tMitigate greenhouse gas emissions and adapt to climate change in all farming systems and value chains. \n-\tIncorporate novel ethics, food habits, demographics and lifestyles into the food chains. \n-\tProduce food on limited farmland and fossil (non-renewable) resources efficiently and profitably. \nSeveral findings from scientific research and practical applications suggest that organic food and farming systems can help in tackling these future challenges.1The 'low external input' approach, risk minimizing strategies and ethically accepted production practices of organic food and farming systemscan help to produce more affordable food for an increasing number of people while minimizing environmental impacts. However, resource efficiency, low-meat diets and reducing food waste are also essential factors that have to be considered. \nFrom a global perspective, organic food and farming systems is still a niche sector, as less than 1% of global farmland is managed organically and only a small proportion of the global population is consuming organic food in significant amounts. Production yields are relatively low, and the goals of organic food and farming systems, described in the principles and standards, are not achieved on every farm. This needs further development based on scientific evidence and good management practices. \nA lot has been done already to develop organic food and farming systems. Nevertheless, to assure, that organic food and farming systems becomes a significant part of the solutions for the future challenges in the food and farming sector, there is still much to do. \nThe Scientific Track at the Organic World Congress 2017 in Delhi, India, will contribute to the global discussion on Organic 3.0, and taking the opportunity to answers some of the challenges in the context of the Indian subcontinent in particular. After a double-blind review, done by 120 reviewers from various disciplines from many experienced research institutions throughout the world, about 183papers from 50 countries have been accepted. \nAll the papers in these proceedings can be also foundon the database "Organic Eprints" (www.orgprints.org). \nThe Scientific Board of the Organic World Congress 2017 Delhi, November 2017
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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 | 0.067 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.011 | 0.009 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".