Use of Remote Sensing and Ground Data in Comprehension of the Flooding in the Bow River Basin, Alberta
Bibliographic record
Abstract
Flooding is a devastating natural hazard throughout the world. Consequently, a flood management system is vital. Here, the aim was to investigate elements of flood management as it pertains to the Bow River in Alberta. The specific objectives included: (i) river flow forecasting at Calgary, (ii) flood extent estimation at Calgary, and (iii) river planform change detection. Analyses revealed that using a multivariable linear regression (MLR) formulated as a function of upstream gauge stations and the station of interest using antecedent flows demonstrated strong relationships (i.e., r2 = 0.93). Furthermore, the flood extent estimation gave a kappa statistic of 0.6, which is reasonable considering that the image was taken 16 days after peak flood time. Lastly, the Bow River planform change detection showed that the 2013 floods caused a higher erosion in the lower Bow River (i.e., 361.62ha) as compared to the upper Bow River area (i.e., 206.01ha).
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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 | 0.001 | 0.001 |
| 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".