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Record W2085063377 · doi:10.2166/wst.2006.317

Impacts of urbanization on West Nose Creek: a Canadian experience

2006· article· en· W2085063377 on OpenAlexaboutno aff
Bert van Duin, José Teodoro Silva García

Bibliographic record

VenueWater Science & Technology · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsnot available
Fundersnot available
KeywordsUrbanizationHydrographStormwaterImpervious surfaceHabitatEnvironmental scienceHydrology (agriculture)STREAMSSurface runoffChannel (broadcasting)Urban streamErosionRelocationGeographyWater resource managementEcologyGeologyEngineeringGeotechnical engineering

Abstract

fetched live from OpenAlex

The lower reaches of West Nose Creek have been subject to urbanization since the 1970s, leading to channel widening and excessive erosion. This paper discusses what would likely happen if urbanization were allowed to continue in the same manner. Comparisons are presented of the channel width and depth for both the upstream rural and downstream urbanizing reaches. Estimates of the evolution of the creek were generated by linking the dominant discharge to the entire shape and volume of the hydrograph that the creek is subjected to rather than solely considering peak discharges. otential remedial measures and stormwater management philosophies are discussed in relationship to instream flow needs (IFNs) initiatives. IFNs are generally developed by relating the amount of suitable aquatic habitat to the quantity of flow. The emphasis has so far been on IFNs for large river systems. Unfortunately, none of the IFN approaches cover streams that are subject to significant urbanization. In urbanized streams the issue is not as much the impacts due to withdrawals but due to significantly increased runoff rates and volumes generated within the urban areas. Examples are provided how fisheries habitat is impacted by the changed hydrologic regime and changed stream morphology.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.643
Threshold uncertainty score1.000

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 studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.004
GPT teacher head0.198
Teacher spread0.194 · 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.

Study designBench or experimental
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

Citations4
Published2006
Admission routes1
Has abstractyes

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