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Hydrodynamic modeling and morphological analysis of Lake Água Preta: one of the water sources of Belém-PA-Brazil

2011· article· en· W2055956031 on OpenAlexaff
Patrícia da Silva Holanda, Cláudio José Cavalcante Blanco, Daniel Onofre de Almeida Cruz, David Franco Lopes, Ana Rosa Baganha Barp, Yves Secretan

Bibliographic record

VenueJournal of the Brazilian Society of Mechanical Sciences and Engineering · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsInstitut National de la Recherche Scientifique
FundersConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsSedimentationBathymetryHydrology (agriculture)Elevation (ballistics)GeologyTerrainFlow (mathematics)GeomorphologySedimentGeographyGeotechnical engineeringOceanographyCartographyGeometryMathematics

Abstract

fetched live from OpenAlex

The main contribution from this paper includes the hydrodynamic modeling and morphological analysis of Lake Água Preta in Belém city, Pará State, Brazil. The lake bathymetry was taken through the data provided by COSANPA (the local sanitation and water supply company) dating back to 1975, and from a 2009 field study. Both bathymetries produced two terrain elevation models, which were used for morphological analysis and hydrodynamic simulations. The morphological analysis has revealed that, from 1975 to 2009, the annual mean rate of sedimentation varies between 23,065 and 29,081 m³/year. Through this result, the sedimentation time of Lake Água Preta, from 2009, has been calculated, which varies between 295 and 381 years, maintaining the same rate of sedimentation, water consumption and pumping. The hydrodynamic model simulated the depths and velocities, showing a slight flow with velocities ranging from 0 to 33 cm/s. This flow is established between water input and output of the lake, which is used as reservoir of Belém city.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.279
Threshold uncertainty score0.170

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.196
Teacher spread0.182 · 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 designSimulation or modeling
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

Citations14
Published2011
Admission routes1
Has abstractyes

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