Innovations in Organic Food Systems for Sustainable Production and Enhanced Ecosystem Services
Bibliographic record
Abstract
This is the proceedings of the international conference ‘Innovations in ORGANIC FOOD SYSTEMS for Sustainable Production and Enhanced Ecosystem Services’. The proceedings are a compilation of peer reviewed articles based on presentations of 18 speakers invited conference speakers and published as a Special Issue of the scientific journal ‘Sustainable Agriculture Research’ by the Canadian Centre of Science and Education. The conference was organized jointly by US Department of Agriculture (USDA), the International Centre for Research in Organic Food Systems, and the Organic Management Systems Group of ASA (ASA-OMS) with financial support from the Organisation for Economic Co-operation and Development (OECD) Co-operative Research Programme on Biological Resource Management for Sustainable Agricultural Systems, whose financial support made it possible for most of the invited speakers to participate in the Conference. The 2-day conference joined speakers from 12 OECD countries and almost 200 conference participants in discussions on new knowledge, innovations, potentials and research needs that will strengthen the link between organic food systems, sustainable production and enhanced ecosystem services. The conference was held as a special symposium of the annual conference of the three societies American Society of Agronomy (ASA), Crop Science Society of America (CSSA) and Soil Science Society of America (SSA), 1-2 November 2014, Long Beach, California.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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 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".