MétaCan
Menu
Back to cohort

Cadastro Territorial Multifinalitário: dados e problemas de implementação do convencional ao 3D e 4D

2012· article· pt· W2032622532 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBoletim de Ciências Geodésicas · 2012
Typearticle
Languagept
FieldEngineering
Topic3D Modeling in Geospatial Applications
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsPolitical scienceHumanitiesBusinessPhilosophy

Abstract

fetched live from OpenAlex

O cadastro territorial multifinalitário é considerado uma ferramenta eficaz para o ordenamento territorial. Com o passar dos tempos, sua aplicação deixa de ser apenas fiscal e passa a ser mais direcionado à gestão territorial, à proteção ambiental e ao desenvolvimento sustentável. Num contexto mais amplo, a implementação do cadastro multifinalitário traz benefícios gerenciais e de planejamento para as organizações governamentais e para o setor privado. Além disso, proporciona benefícios diretos aos cidadãos, melhoria no acesso às informações territoriais, mais precisão na avaliação da propriedade em casos de compra ou venda, identificação da localização de serviços básicos, entre outros. Considerando esses aspectos, este artigo tem como finalidade descrever de uma forma geral, os componentes do cadastro territorial multinalitário e apresentar algumas reflexões acerca dos benefícios da sua implementação. Descreve, ainda, os Cadastros 3D e 4D e discute alguns problemas sobre a implantação do cadastro multifinalitário no Brasil.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.630
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.024
GPT teacher head0.282
Teacher spread0.258 · 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