MétaCan
Menu
Back to cohort

Use of Stabilized Stream-Monitoring Sections to Monitor Annual Streamflow on the Alberta Boreal Plain

2013· article· en· W2035723752 on OpenAlexaffabout
W. Paul Dinsmore, Mark Serediak, Gordon Putz, Ellie E. Prepas, Daniel Smith

Bibliographic record

VenueJournal of Cold Regions Engineering · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of AlbertaUniversity of SaskatchewanLakehead University
Fundersnot available
KeywordsStreamflowHydrology (agriculture)Channel (broadcasting)Environmental scienceDebrisSTREAMSCoastal plainErosionDebris flowBorealStage (stratigraphy)GeologyGeomorphologyOceanographyGeographyGeotechnical engineering

Abstract

fetched live from OpenAlex

Streamflow in small forested watersheds on the Boreal Plain of western Canada can be a challenge to monitor due to high variation in flow, shifting channel morphology, aufeis obstructions, shallow channel depth, and debris in the channel. Intensive monitoring in natural channels can overcome some of these problems, but frequent assessment and recalibration of streamflow-stage relationships are necessary. Experience over 8 years indicates that in-channel structures designed to provide a stabilized stream-monitoring section (SSMS) proved beneficial to monitoring efforts during the ice-free season. The SSMS facilitated accurate gauging of the highest and lowest flows encountered during this period, provided a relatively stable foundation against streambank and channel erosion, and allowed passage of fish and the majority of debris and sediments. Functionality of the structures was markedly limited during ice-in conditions, but was improved with the addition of fitted canopies and propane heating systems.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.649
Threshold uncertainty score0.699

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.023
GPT teacher head0.207
Teacher spread0.184 · 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
Published2013
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Cold Regions EngineeringSame topicCryospheric studies and observationsFrench-language works237,207