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Record W4220720246 · doi:10.5194/egusphere-egu22-10998

The November 2021 floods in British Columbia, Canada: observations, mechanisms and probabilities

2022· preprint· en· W4220720246 on OpenAlexaffabout
Steven Weijs, Daniel Kovacek

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAntecedent moistureFlood mythNatural disasterReturn periodAntecedent (behavioral psychology)River floodEvent (particle physics)Environmental scienceSnowGeographyClimatologyHydrology (agriculture)MeteorologyCartographyGeologyDrainage basinArchaeologyPsychology

Abstract

fetched live from OpenAlex

In this presentation, some insights into the various contributing factors of the floods in southwestern British Columbia, Canada, will be shared. These floods followed a large atmospheric river event, combined with high antecedent soil moisture and rain on snow mechanisms. In some locations, including some that were affected by extensive wildfires in the preceding summer, estimated return periods of river flows during this event exceeded 2000 years. Various alternative estimations of this return period will be presented, conditional on various assumptions and side information Due to the large scale disruption of infrastructure, this event is expected to be (one of) the costliest natural disaster in history for Canada. This presentation is informed both by probabilistic analysis of the various factors and anecdotal evidence based on an aerial reconnaissance of the flood affected area.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.009
GPT teacher head0.184
Teacher spread0.175 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2022
Admission routes2
Has abstractyes

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