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Record W4309269310 · doi:10.54097/hset.v17i.2462

Causes and Effects of the November 2021 Pacific Northwestern Floods in British Columbia

2022· article· en· W4309269310 on OpenAlexaboutno aff
Chuqiao Lai, Xi Liu, Wuyang Su, Anni Zheng

Bibliographic record

VenueHighlights in Science Engineering and Technology · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsnot available
Fundersnot available
KeywordsFlooding (psychology)DamagesGeographyPrecipitationPopulationFlood mythEmergency managementMeteorologyArchaeologyDemographyEconomic growth

Abstract

fetched live from OpenAlex

Global warming can result in high frequency in precipitation in some areas, and as a result of high precipitation, floods may occur more frequently and may cause more damage to urban areas. The Pacific Northwestern Flooding event that happened between November 14, 2021, to December 17, 2021, is a series of recent flooding events, and it can reasonably be the most expensive flooding event in British Columbia history. This study used QGIS tools to analyze the topographical characteristics of Vancouver in contribution to the severity of the floods. This study also organized reports on the infrastructure, population, and economic impacts of the floods in British Columbia, where Vancouver settled. Results show that Vancouver is generally low in altitude, with some areas in pot shape, making it easy for floods to invade. There was also an increase in rainfall frequency in Vancouver in the past 50 years, which aided the severity of floods. The flooding event in British Columbia has led to transportation damages, fatalities and injuries, and economic losses. Some suggestions for urban planning and administration in conscious of the presence of more severe flooding events are proposed such as comprehensive emergency management program.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.028
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.002
GPT teacher head0.168
Teacher spread0.166 · 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 teacher head, 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 routes1
Has abstractyes

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