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Record W4385992817 · doi:10.18174/574096

Rendement regulier verpachte landbouwgrond, 1990-2020

2022· report· nl· W4385992817 on OpenAlexaff
H.J. Silvis, M.J. Voskuilen

Bibliographic record

Venuenot available
Typereport
Languagenl
FieldEnergy
TopicEnergy, Environment, Agriculture Analysis
Canadian institutionsImpact
Fundersnot available
KeywordsAgricultural economicsChristian ministryRate of returnLeaseCapital (architecture)Yield (engineering)AgricultureEconomicsBusinessGeographyFinanceArchaeologyPolitical science

Abstract

fetched live from OpenAlex

In het overleg Pachtnormen 2020 heeft de vaste commissie voor Landbouw, Natuur en Voedselkwaliteit onder meer aandacht gevraagd voor de verhouding tussen het rendement van verpachting en de vermogensrendementsheffing over de pachtopbrengst in box 3. Dit onderzoek analyseert het directe en indirecte rendement van verpachting van landbouwgrond in de afgelopen drie decennia.Het nominale rendement van regulier verpachte grond in de periode 1990-2020 is berekend op ongeveer 7% per jaar.In a consultation on the 2020 Lease Standards, the standing committee for the Ministry of Agriculture, Nature and Food Quality has drawn attention to the relationship between the rate of return on leased land and the capital yield tax in box 3.This study analyses the direct and indirect returns of leased agricultural land over the past three decades.The nominal rate of return on leased land has been estimated at approximately 7% per annum during the period 1990-2020.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.145
Threshold uncertainty score0.288

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0140.007

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 head0.013
GPT teacher head0.229
Teacher spread0.216 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2022
Admission routes1
Has abstractyes

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Same topicEnergy, Environment, Agriculture AnalysisFrench-language works237,207