Buitenbeentjes en boegbeelden; advies over megabedrijven in de Nederlandse land- en tuinbouw
Bibliographic record
Abstract
Het rapport wordt afgesloten met bedrijfsrapportages. 1. Wilhelminadorp (Zuid-Beveland, 1700 ha groot. Naast akkerbouw ook zaaizaad, en 162 ha mosselpercelen in Oosterschelde). 2. Breukers (In Slowakije een bedrijf van 700 ha). 3. Teboza (Helden, aspergeteelt)100 ha asperges, 180 ha tevens in Tsjechië). 4. Van de Bilt Zaden en Vlas (vestiging in Sluiskil; teelt van vlas in Flevopolders en Zeeuws-Vlaanderen; tevens een bedrijf in Frankrijk van 3000 ha). 5. Van den Oever Boomkwekerijen (Haaren, 130 ha laanbomen, heesters). 6. Seasun (Kapelle, 50 ha kastuinbouw paprika's en tomaten). 7. Vrebamelkvee (Vredepeel, 1000 melkkoeien, 675 jongvee). 8. Koepon (Aduarderzijl, 175 ha, met twee bedrijven in voormalig Oost-Duitsland, 1 in Schotland en 1 in canada, en startend in Polen; tevens in bezit van makelaardij AgriPon) 9. Den Eelder (Well, 450 koeien met eigen productie en afzet van zuivel). 10. Buist (Noordlaren, grootste natuurboer van Nederland en tevens grootstye biologische vleeshouder; 1200 ha en ruim 600 stuks Limousin). 11 Varkenshouderij in Noord-Limburg (eigen bedrijf met 200 zeugen en op 14 locaties gepachte stallen, tevens vestiging in Oost-Duitsland met 7000 zeugen en 2500 ha akkerbouw)
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.002 |
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".