EVALUATION OF ALTIMETRIC ACCURACY OF DIGITAL SURFACE MODELS IN THE URBAN AREA OF CAMPO GRANDE/BRAZIL
Bibliographic record
Abstract
Os Modelos Digitais de Superfície são utilizados em diversas aplicações, sendo essenciais para modelagem hidrológica. Descrevemos a precisão altimétrica de diferentes MDSs na área urbana de Campo Grande, Mato Grosso do Sul, Brasil. Como conjunto de dados de referência, usamos dois conjuntos de dados distintos com coordenadas GNSS 3D. Os modelos testados foram Tandem-X, ALOS AW3D30, SRTMc, TOPODATA, SRTM v.3 e Aster GDEM v.2. Estimamos as discrepâncias verticais em relação ao conjunto de dados de referência, usando as seguintes métricas: discrepância vertical mínima e máxima e média; DP; RMSE; e o padrão brasileiro de precisão cartográfica para produtos cartográficos digitais (PEC-PCD). O ALOS AW3D30 apresentou os melhores resultados em termos de RMSE (1,77 m), seguido pelo Tandem-X (RMSE de 2,80 m). Os demais MDSs apresentaram RMSE variando de 3,6 a 7,5 m. O ALOS AW3D30 e o Tandem-X mostraram compatibilidade com a escala 1:50.000, enquanto os demais modelos com a escala 1:100.000, conforme PEC-PCD classe A. Demonstramos que tanto o ALOS AW3D30 quanto o Tandem-X podem ser utilizados para realizar projetos relacionados à modelagem hidrológica em escalas menores ou iguais a 1:50.000. Já os demais MDSs devem ser adotados em projetos que envolvam escala menor ou igual a 1:100.000.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".