AN URBAN RAINFALL STORM FLOOD SEVERITY INDEX Prepared by:
Bibliographic record
Abstract
I would like to thank my thesis supervisor Nistor for his help, trust, guidance and, suggestions throughout my Masters program. I would like to thank my parents for their patience, encouragement and considerable help throughout this study and my Masters program. I would like to thank my brother for his help in formatting media files. I would like to thank Benoît Pagé for his input, company and for reviewing my thesis work. I would like to thank Vincent Gabriel-Vallée for his help in producing the polygon storm dataset and providing programming guidance. I would like to thank Kije Sipi Ltd and RadHyPS Inc for putting their resources at my disposal during the course of this study. I would like to thank Sid Lodewyk for his input during the data gathering phase. I would like to thank Hongliang Wang for providing data, maps, information as well as answering questions during the data gathering phase. I would like to thank The City of Edmonton for allowing their Drainage Services
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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 | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 0.009 |
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".