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Monitoring the 1997 flood in the Red River Valley using hydrologic regimes and RADARSAT imagery

2005· article· en· W1990316368 on OpenAlexaffvenueabout
Bradley A. Wilson, Harun Rashid

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsLakehead University
Fundersnot available
KeywordsFlood mythFlooding (psychology)Hydrology (agriculture)FloodplainSurface runoffEnvironmental scienceSatellite imageryFlash floodRemote sensingGeologyCartographyGeographyArchaeology

Abstract

fetched live from OpenAlex

In this study, we attempt to relate hydrologic regimes of the 1997 flood in the Red River Valley to the areal extent of flooding, determined from RADARSAT imagery. We obtained ten scenes of RADARSAT imagery, from 27 April to 1 July, including bitmaps delineating flooded areas for each date, from the Manitoba Centre of Remote Sensing. These images were co‐registered using an image‐to‐image registration process. By overlaying these flood maps in chronological order, we compared the areal extent of flooding with the hydrologic regimes of the Red River, expressed as relative depths of flooding above the bankful stage at selected gauging stations. The results of the study indicated that the area of flooding on 4 May (1,984 km 2 ) corresponded well with the highest flood levels at several gauging stations. A previous scene on 27 April showed a larger area under water, but visual inspection of the processed imagery indicated a lack of conformity between flood level regimes and the areal extent of flooding on this date due to surface detention of pre‐flood storm runoff. Thus, the RADARSAT imagery represented the flood regimes adequately only when its interpretations were combined with hydrologic analysis and visual inspection of surface characteristics of the floodplain.

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)0.001
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 score0.310
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.010
GPT teacher head0.199
Teacher spread0.190 · 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

Citations30
Published2005
Admission routes3
Has abstractyes

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