Análise da influência da malha computacional na simulação do rompimento de uma barragem por meio do software IBER. Estudo de caso: barragem de Barra do Braúna
Bibliographic record
Abstract
The problems due to the breaking of the barrages are happening in a recurring way, becoming two of the most discussed issues in the environmental area. In this way, it is necessary to analyze the risk and safety of the barrages so that safety measures are taken as long as the possibility of breakage is foreseen. Thus, the computational simulation appears as a way to analyze these ruptures based on the rupture hydrograph. In this context, the objective of this article is to analyze the influence of computational evil in the simulation of the hypothetical rupture of the barrage of the Barra do Braúna Hydroelectric Plant located on the Pomba river, in a stretch surrounding the city of Santo Antônio de Pádua and also, to assess the impacts caused pela onda de cheia in the referred city, by means of the IBER software. Key-words: Breaking of Barragem. Pomba River. IBER software.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".