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Record W2466188612 · doi:10.21168/rbrh.v18n2.p67-82

Incorporação do Impacto da Rede de Reservatórios Superficiais Artificiais de Caráter Intranual na Modelagem Hidrológica Chuva-Vazão

2013· article· pt· W2466188612 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

VenueRevista Brasileira de Recursos Hídricos · 2013
Typearticle
Languagept
FieldEnvironmental Science
TopicGeography and Environmental Studies
Canadian institutionsImpact
Fundersnot available
KeywordsPhysicsEnvironmental science

Abstract

fetched live from OpenAlex

A hydrologic model’s ability to describe the process-
\nes encompassing the transformation of precipitation into
\nflow rates depends on the assumptions, structure and for-
\nmulations it uses. Models of the spatially distributed type
\nare very attractive. However, the mathematical simplifica-
\ntions used may generate errors when the represented basin's
\nfeatures are very heterogeneous due to natural characteris-
\ntics or to anthropic activity. The river regime in the semi-
\narid Northeast of Brazil has undergone changes due to the
\nconstruction of superficial artificial reservoirs, most of which are small reservoirs of an intra-annual nature.
\nThose small reservoirs make modeling difficult.
\nIn this paper we propose a change to the structure of the
\nSMAP model aiming to explicitly incorporate the represen-
\ntation of small damming. This representation is done by
\ninserting a reservoir into the mathematical model with the
\noperating characteristics which are representative of small
\ndamming behavior. The dimension of that reservoir is a
\ngradable parameter (h RPA) aiming to identity the effect of
\nthis intra-annual regularization.
\nThe proposed model, SMAP-RPA, consists of expanding the
\nSMAP model in its monthly version. Therefore, determin-
\ning the results from the RPA component improves the mod-
\nel’s performance and makes it more realistic when compared
\nto SMAPm's performance. As a result, in cases where the
\ndams in the hydrographic basin do not produce significant
\nvariation in the hydrograph, the “hRPA” parameter is null
\nand the SMAP-RPA operation is the same as in SMAPm.
\nAiming to ascertain the proposed model's validity, we de-
\nveloped a case study by formulating two scenarios accord-
\ning to the flow rate series and the characteristics of 18 hy-
\ndrographic basins located in the state of Ceará. The results
\nsupport the efficiency of small damming representation and
\nthe ensuing efficiency improvement in the adjustments to
\nobserved and calculated series. In this paper we have also
\nnoticed an important scale effect. The basins which were
\naffected most by small damming were those with a drainage
\narea of less than 5,000 km2. The impact of this small
\ndamming on larger scale basins is not detected by the mod-
\nel, which makes the “h RPA” parameter null

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), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.003

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 head0.022
GPT teacher head0.246
Teacher spread0.224 · 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