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Land system changes in the context of urbanisation: Examples from the peri-urban area of Greater Copenhagen

2006· article· en· W2087830893 on OpenAlexaff
Anne Gravsholt Busck, Søren Bech Pilgaard Kristensen, Søren Præstholm, Anette Reenberg, Jørgen Primdahl

Bibliographic record

VenueGeografisk Tidsskrift-Danish Journal of Geography · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsRoyal Ottawa Mental Health Centre
Fundersnot available
KeywordsUrbanizationGeographyContext (archaeology)RecreationAgricultureAgricultural landLand useRural areaPopulationAgricultural productivityEnvironmental planningEnvironmental protectionAgricultural economicsEconomic growthCivil engineeringEconomicsEcology

Abstract

fetched live from OpenAlex

Abstract Peri-urban areas are characterised by great heterogeneity and rapid changes of land use. Furthermore, population composition changes as peri-urban areas offer attractive residential alternatives to city centres or more remote locations. The dynamic processes leave peri-urban areas in an in-between situation, neither city nor countryside and home to a range of functions, spanning from agricultural production to residential and recreational areas. The paper investigates the urbanisation of agricultural areas in the Greater Copenhagen region based on quantitative data collected on agricultural properties in nine study areas between 1984 and 2004. The overall conclusion is that agricultural land use has continued largely unaffected by the processes of urbanisation. However, most of the production is concentrated on a few very large full-time farms. In addition, the economic activities have been greatly diversified over the last three decades. The structural components of the areas (land use and landscape elements) thus appear more resilient than the socio-economic system (declining number of full-time farmers and increasing number of owners engaged in other gainful activities). However, at some point this discrepancy will disappear and rapid land use changes may be expected.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.184
Teacher spread0.174 · 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 teacher head, 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

Citations96
Published2006
Admission routes1
Has abstractyes

Explore more

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