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Record W2982222837 · doi:10.14393/sn-v31-2019-46344

Caracterização geotécnica e geoambiental da bacia do Córrego São Pedro- Uberlândia/MG: contribuição para elaboração do plano de drenagem urbana

2019· article· pt· W2982222837 on OpenAlexaboutno aff
Ana Clara Mendes Caixeta, Vanderlei de Oliveira Ferreira, Luiz Nishiyama

Bibliographic record

VenueSociedade & natureza · 2019
Typearticle
Languagept
FieldEnvironmental Science
TopicGeography and Environmental Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPhysicsHumanitiesGeomorphologyGeologyArt

Abstract

fetched live from OpenAlex

This article presents the results of a research aimed at geotechnically and geoenvironmentally characterizing the São Pedro stream basin, located in Uberlândia/MG - Brazil, from the physical environment’s attributes and properties, with a view to using land as a means of rainfall infiltration. The used methodology focuses on analytical mapping and generating cartographic documents that are individually elaborated and analyzed. Besides that, the characterization of unconsolidated materials (in situ and in laboratory) and coefficient of permeability (k) tests (in situ with a Guelph permeameter) were performed. With this data at hands, a quali-quantitative analysis of the physical environment’s features was made through the study of its attributes and their representation in maps. Variables related to infiltration and runoff processes were privileged. For the area of the São Pedro stream basin, values of k varying between 10-4 and 10-5 cm/s were found. The adequacy-to-infiltration map showed the areas next to the streams and in the stream’s mouth as non-adequate or slightly-adequate. On the other hand, the areas close to the drainage divides of the basin showed to be highly-adequate. The center of the basin and the region between the Jataí and the Lagoinha streams are also highly-adequate to infiltration. These areas with higher adequacy are appropriate to the implementation of structural and non-structural measures that seek to reduce flood risks. Thus, we hope to contribute to urban planning in this study area through these geoenvironmental maps.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.041
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.007
GPT teacher head0.228
Teacher spread0.221 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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