A GIS-based Approach To River NetworkFloodplain Delineation
Bibliographic record
Abstract
The main goal of this continuing study is to develop a GIS-based procedure for delineating and displaying in 3-D the desired floodplain zones of river networks using data generated by the HEC-2 numerical model. The methodology is applied to the Bear Brook sub-watershed of the South Nation river system, located near Ottawa, Ontario, Canada. First, HEC-2 data are imported and reproduced in HEC-RAS. Next, HEC-RAS in-stream data are geo-referenced and mapped in GIS domain and then integrated with digital elevation model (DEM) over-bank data to build a terrain triangular irregular network (TIN) model. This integrated terrain model is then overlaid with water surface TIN for selected storm events for 3-D floodplain visualization. Data query models, relating to ‘local flood depth’ and ‘channel section’ information, are also constructed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".