Innovative, sustainable, and circular agricultural systems for the future
Bibliographic record
Abstract
Abstract This special issue presents the outcomes from " Designing sustainable and circular agricultural systems for the year 2100 ," the joint scientific workshop of ISOFAR, the Thünen-Institute, and INRA-Morocco, which was held from November 14 to 16, 2019 in Marrakesh, Morocco. Nineteen scientists from a broad array of background and nationalities came together with the understanding that food security globally is at risk, especially in the post-2050 timeframe. Current concepts, strategies, measures, and scientific efforts carried out by governments, NGOs, businesses, and societies do not deliver satisfying solutions for how to sustainably produce enough healthy and affordable food to support the global population. With the economic and social impact of the Covid-19 pandemic in 2020, it became even more evident that food security is a challenge. This workshop took an innovative approach to addressing the challenges of future agriculture by considering sustainable, circular agricultural systems. Participants presented research results on algae-based food, edible insects, mushrooms, novel concepts for nutrient management, bioreactor-based farming, sustainable food culture, as well as sensor- and remote-controlled automatic food production. This special issue presents the papers contributed to the workshop and the results of the discussions.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".