Regeneratieve landbouw : ervaringen en lessen uit een Community of Practice
Bibliographic record
Abstract
van landbouw waarin de boer probeert op alle biofysische en sociaal-economische aspecten van duurzaamheid positief te scoren.RL richt zich niet op de genomen maatregelen maar op de uitkomsten daarvan.In de afgelopen vier jaar is een Community of Practice van 18 boeren gevolgd, die al kortere of langere tijd in deze richting werken.Het ging om verschillende bedrijfstypen, grondsoorten en regio's en ook om heel verschillende typen maatregelen die genomen zijn om de gewenste uitkomsten te halen.Hun aanpak biedt inspiratie voor andere boeren die ook richting RL willen bewegen, al bleek het monitoren van de uitkomsten een grote uitdaging.Regeneratieve Agriculture (RA) is a form of agriculture in which the farmer tries to positively score on all biophysical and socioeconomic aspects of sustainability.RA focuses on the outcomes, not on the measures taken.In the past four years, a Community of Practice of 18 farmers has been followed, who have been working towards RA for a shorter or longer period of time.Different farm and soil types and regions were involved as well as very different types of measures that had been taken to reach the outcomes desired.Their approach offers inspiration for other farmers that also want to move into the direction of RA, although monitoring their outcomes appeared a big challenge.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.004 |
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".