Onderzoek naar de gevolgen van hoge energieprijzen in de glastuinbouw in de periode medio 2021 tot en met het eerste kwartaal van 2023
Bibliographic record
Abstract
Dit onderzoek laat zien wat de directe en indirecte gevolgen waren van de grote energieprijsstijgingen tussen medio 2021 en het eerste kwartaal van 2023 voor de glastuinbouw in Nederland.Het aantal faillissementen en bedrijfsbeëindigingen zonder faillissement is in 2022 wel iets gestegen, maar grote aantallen faillissementen zijn uitgebleven.De kosten maar ook de verkoopprijzen zijn sterk gestegen en er hebben op veel bedrijven aanpassingen plaatsgevonden, zowel tijdelijke aanpassingen in de teelt als ook meer permanente investeringen in energiebesparing.Met name in de sierteeltsector hebben de hoge energieprijzen een negatief effect op het inkomen gehad.This study reveals the direct and indirect effects of the large increases in energy prices between mid-2021 and the first quarter of 2023 on greenhouse horticulture in the Netherlands.There was a slight increase in the number of bankruptcies and of non-bankruptcy business closures during 2022, but bankruptcies were not widespread.Costs have risen sharply but so have selling prices, and many businesses have brought in new measures such as temporary modifications to their cultivation practices as well as more permanent investments in energy saving practices.High energy prices have had a particularly negative impact on income in the ornamentals sector.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".