Influence of hydrometeorological controls on debris flows near Chilliwack, British Columbia
Bibliographic record
Abstract
This study aims to identify hydrometeorological variables near Chilliwack, BC which have initiated past debris flows in order to gain insight about conditions that could inform emergency planning and adaptation in future.A database of storms between 1980 and 2007 and their hydrometeorological characteristics including storm total rainfall and duration, intense rainfall total and duration, and 1 to 4 week cumulative antecedent rainfall were compiled.Stepwise logistic regression was used to determine a model which isolated intense rainfall total and occurrence of storms during the rain-on-snow season as the most significant variable distinguishing between debris flow and non-debris flow storms.However, the low predictive power of this analysis suggests that other characteristics, such as land-use, sediment supply, and snow melt may play a large role in debris flow initiation in this region.Dr. Andrew Cooper for his invaluable methodological input and incredible ability to demystify statistics.Prof. Peter Anderson for introducing me to the realm of emergency communications and planning.The Climate, Oceans, and Paleo-Environments Lab for listening and providing ideas and improvements for my project.I especially appreciate the help of Brad Griffin, Brian Bylhouwer, and Ben Cross for their brilliant help with teaching me the trials and tribulations of writing code.Also, a thanks to My Lam
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".