MétaCan
Menu
← Back to cohort
Record W7115922761 · doi:10.4224/40003937

City of Calgary catch basin performance study

2024· report· en· W7115922761 on OpenAlexaffvenueabout

Bibliographic record

VenueNPARC · 2024
Typereport
Languageen
Field
Topic
Canadian institutionsNational Research Council CanadaInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsInletStructural basinInflowHydrology (agriculture)Work (physics)Instrumentation (computer programming)

Abstract

fetched live from OpenAlex

This report provides catch basin inflows for five catch basin inlet configurations and four curb cut configurations at six grades ranging from 0.5 - 10.0% and cross-slopes of 0.0, 2.0 and 4.0%. Catch basin inflow rating curves were developed by testing a mock-up of a roadway at the Ocean, Coastal and River Engineering Research Centre of the National Research Council of Canada. Water flows ranging from 0.0002 - 0.39 m³/s were delivered to a model roadway to quantify the conveyance of the catch basin inlets for various roadway grades and cross-slopes. The report covers a review of the experimental setup, including details on the model roadway that was used to perform the tests. A review of the instrumentation used and the measurements performed in this work follows. The five catch basin inlet configurations and four curb cut configurations used in the study were examined. A preliminary analysis including an assessment of experimental uncertainties was performed. The detailed results from each of the five catch basin inlet combinations and four curb cut combinations were provided. Finally, a comparison of the performance of the drainage solutions covered in this report is performed.

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)0.001
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: Other · Consensus signal: none
Teacher disagreement score0.308
Threshold uncertainty score0.619

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.002

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 head0.053
GPT teacher head0.321
Teacher spread0.268 · 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
GenreOther

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

Citations0
Published2024
Admission routes3
Has abstractyes

Explore more

Same venueNPARC→French-language works237,207→