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O estudo do consórcio entre municípios de pequeno porte para disposição final de resíduos sólidos urbanos utilizando sistema de informações geográficas

2003· dissertation· pt· W1496807393 on OpenAlexaff
Mauro Kenji Naruo

Bibliographic record

Venuenot available
Typedissertation
Languagept
FieldEnvironmental Science
TopicMunicipal Solid Waste Management
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsconsPolitical scienceComputer scienceProgramming language

Abstract

fetched live from OpenAlex

índole e inigualável dedicação, ao meu irmão Keniti, de invejável paciência e perseverança, pessoas em quem sempre me espelharei, e também, a minha sempre Cíntia, sempre. A AG GR RA AD DE EC CI IM ME EN NT TO OS SPrimeiramente, só tenho que agradecer aos meus pais, que sob sacrifício tiraram do suor e da terra a oportunidade que eles não tiveram, mas fizeram questão de dar aos seus filhos.Ao meu irmão, por ser o espelho de toda a minha vida na luta por oportunidades que outrora, sob sol escaldante, nem nos atrevíamos a sonhar.Ao Prof. Edson, pela orientação e pelo desafio que foi proposto neste trabalho.Agradeço de forma generalizada, a todas as Prefeituras e empresas privadas e públicas, pela disposição em fornecer as informações necessárias para o desenvolvimento desta pesquisa de mestrado.

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)0.009
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.030
GPT teacher head0.288
Teacher spread0.259 · 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

Citations6
Published2003
Admission routes1
Has abstractyes

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