Regenerative agriculture in Europe : An overview paper on the state of knowledge and innovation in Europe
Bibliographic record
Abstract
Regenerative agriculture (RegenAg) is on the rise, aiming to make farming sustainable. But what isRegenAg? How does RegenAg compare to other agricultural concepts? How does RegenAg differ acrossEurope? Can RegenAg be economically viable? And can it be measured? This report identifies that it isdifficult to assess the current state of a concept that does not have a clear definition. We compared RegenAgto agroecology, conservation agriculture and organic farming. The most striking differentiation seems to bethat RegenAg is defined by its outcomes. This provides freedom to farmers, while considering the contextspecificity of RegenAg. Measuring actual outcomes is hard to accomplish and often has a weak relation withfarm level measures. Hybrid measuring approaches based on farm measures in combination with farm datacan be useful. This report concludes with recommendations to consider RegenAg as a set of objectives,rather than a set of measures and to start an EU-wide indicator system.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".