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Record W4285265340 · doi:10.18174/570648

Pachtnormen 2022 : berekening hoogst toelaatbare pachtprijzen voor los land, agrarische bedrijfsgebouwen en agrarische woningen

2022· report· nl· W4285265340 on OpenAlexaff
H.J. Silvis, R.W. van der Meer, M.J. Voskuilen

Bibliographic record

Venuenot available
Typereport
Languagenl
FieldEnvironmental Science
TopicEnvironmental Conservation and Management
Canadian institutionsImpact
Fundersnot available
KeywordsHumanitiesForestryArtGeography

Abstract

fetched live from OpenAlex

De hoogst toelaatbare pachtprijzen voor 2022 zijn berekend conform de uitgangspunten van het Pachtprijzenbesluit 2007. De berekende pachtnormen 2022 van los bouw- en grasland zijn in vijf van de veertien pachtprijsgebieden hoger, en in acht gebieden lager dan de pachtnormen 2021. Voor zeven gebieden is de verandering beperkt tot maximaal plus of min 3%. De grootste daling geldt voor het Zuidwestelijk akkerbouwgebied (-23%), dat vorig jaar nog de grootste stijging (25%) noteerde. Dit jaar zijn de grootste stijgingen voor het Centraal veehouderijgebied (17%) en het Hollands/Utrechts weidegebied (12%). De veranderingen in de pachtnormen van 2021 naar die van 2022 zijn vooral bepaald door de verschillen in grondbeloning tussen het jaar 2015 en het jaar 2020.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.050
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0050.003
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0340.010

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.

Opus teacher head0.017
GPT teacher head0.242
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations5
Published2022
Admission routes1
Has abstractyes

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