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Record W2948019466 · doi:10.3808/jeil.201900002

Flood-Drought Hazard Assessment for a Flat Clayey Deposit in the Canadian Prairies

2019· article· en· W2948019466 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Environmental Informatics Letters · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsUniversity of Regina
FundersUniversity of Regina
KeywordsImpervious surfaceEnvironmental scienceFlash floodHydrology (agriculture)PondingPrecipitationWater balanceFlood mythSurface runoffRelative humidityWater levelDrainageGeologyMeteorologyGeography

Abstract

fetched live from OpenAlex

Dry climate, clayey soil, and flat topography govern water balance in the southern part of the Canadian Praries. The main purpose of this work was to assess flood-drought hazard using Regina as a typical urban centre in the region. Results indicate that extreme weather patterns are frequent and meteorological parameters have changed from 1970 to 2015: precipitation (+50 mm), air temperature (+0.9oC), relative humidity (+6%), wind speed (-1.35 km/hr), and solar radiation (+0.9 MJ/m2). In the dry climate (Dfb), 77% of the total annual precipitation (386 mm/year) occurs from April to September. The runoff coefficient of 0.6 relates to 63% impervious areas (commercial, industrial and residential) and 35% near-impervious areas (open spaces with low hydraulic conductivity). The flat topography (570 m through 600 m asl over 124 km2) along with a low channel slope of up to 0.4% results in water ponding during short-term and high-intensity rainfalls. Water is managed through the Wascana Creek that holds 98% of the total water volume (84 x 106 m3) in the city. From April to September, volume fluctuations depend on antecedent water levels and meteorological conditions. The city has recently received several events of flash floods (2010 and 2014) and long-term droughts (1984 and 2017). The negative average change in storage indicates drought-like conditions during spring-summer.

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.

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)0.001
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 score0.129
Threshold uncertainty score0.511

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.007
GPT teacher head0.215
Teacher spread0.208 · 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