REGIONALIZATION OF THE IDF CURVES FOR THE PROVINCES OF SANTO DOMINGO DE LOS TSACHILAS AND ESMERALDAS
Bibliographic record
Abstract
In this study, two methodologies were compared for the regionalization of precipitation data in the provinces of Esmeraldas and Santo Domingo de los Tsáchilas.40 meteorological stations were initially selected, but 32 were used due to data availability.The main objective was to determine which methodology offered greater precision in the evaluated models.Process 1, based on the methodology of Nuñez Neira and Corapi, was compared with process 2, developed according to Velasco Ramos and Garaicoa Velásquez.The latter stood out for its comprehensive approach, which included the identification and correction of atypical data using the Hydrognomon program, as well as the adjustment of distributions for different return periods.Although the interpolation methods applied in process 2, such as Inverse Distance Weighted (IDW) and Ordinary Kriging (KO), presented a greater relative error than in process 1, the robustness of process 2 in data management and correction of anomalous values turned out to be a decisive factor for their choice.Another advantage of process 2 was the implementation of the US method for estimating missing data, which added reliability to the results obtained.In conclusion, despite the error limitations in the interpolation methods, process 2 was considered more suitable due to its comprehensive approach and its ability to handle data accurately, detecting and correcting irregularities.These results provide a more reliable tool for the regionalization of precipitation in the study area, thus improving the basis for future hydrological research.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".