Impactanalyse stoppen van gebruik van bestrijdingsmiddelen in grondwaterbeschermingsgebieden : Quickscan
Bibliographic record
Abstract
De quickscan heeft als doel een eerste voorlopig inzicht te krijgen in de impact van een landelijk gebruiksverbod van bestrijdingsmiddelen in grondwaterbeschermingsgebieden (GWB-gebieden).Nederland heeft 158 GWB-gebieden met een areaal van 94.310 ha.Deze gebieden komen in heel Nederland voor, voornamelijk op lichtere (zand)gronden.Van de 39 stoffen die in ten minste 3 grondwatermonsters boven de drinkwatersignaleringsnorm zijn aangetroffen in de periode 1963-2022, zijn 10 stoffen afkomstig van bestrijdingsmiddelen die nog steeds een toelating hebben als gewasbeschermingsmiddel of biocide binnen Nederland.Daarvan zijn 6 stoffen een herbicide of een metaboliet van een herbicide.De gevolgen van een verbod op het gebruik van bestrijdingsmiddelen in GWB-gebieden zijn het grootst voor meerjarige teelten en gewassen met een hoog saldo.The quickscan aims to gain a first general understanding of the impact of a nationwide pesticide use ban in groundwater protection areas (GWP areas).The Netherlands has 158 GWB areas with an area of 94,310 ha.These areas occur throughout the Netherlands, mainly on the lighter (sandy) soils.Of the 39 substances detected in at least 3 groundwater samples above the drinking water signalling value in the period 1963-2022, 10 substances are from pesticides that still have an authorisation as a plant protection product or biocide within the Netherlands.Of these, 6 substances are a herbicide or a metabolite of a herbicide.The consequences of a ban on pesticide use in GWB areas are greatest for perennial crops and high balance crops.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".