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Record W1945966986

Still Creek as a Water-Disposal Machine: An Archival Survey 1913-1988

2012· article· en· W1945966986 on OpenAlexaffabout
Kevin Chan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicWater Governance and Infrastructure
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSewerageDrainageDredgingDrainage system (geomorphology)Groundwater rechargeStormwaterBlackwaterNatural (archaeology)Hydrology (agriculture)ArchaeologyWater resource managementGroundwaterEnvironmental planningGeographyEnvironmental scienceEngineeringGeologyAquiferEnvironmental engineeringOceanographySurface runoff
DOInot available

Abstract

fetched live from OpenAlex

Still Creek, located in Vancouver, British Columbia is one of only two creeks that still flow partially above ground in the city. Although it is in some respects an unremarkable urban waterway, its progressive development into a storm water conveyance channel provides insight into Vancouver’s changing relationship with water since the early twentieth century. I argue that the role of Still Creek as a major drainage trunk has largely been obscured by the system within which the drainage system has been incorporated. I seek to investigate the ways in which Still Creek has been incorporated into the district’s drainage system through a particular (and intentional) combination of technological and natural systems and how it can be better understood as a water-disposal machine. I consider both physical changes (such as the dredging, straightening, and culverting undertaken by the Greater Vancouver Sewerage and Drainage District to increase its maximum water carrying capacity), and changes in popular perception that occurred over the course of the twentieth century.

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)0.001
metaresearch head score (Gemma)0.002
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.899
Threshold uncertainty score0.204

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.010
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.025
GPT teacher head0.310
Teacher spread0.285 · 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
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
Published2012
Admission routes2
Has abstractyes

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