MétaCan
Menu
← Back to cohort
Record W2317926972 · doi:10.1061/40926(239)174

Investigating Ship Induced Scour in a Confined Shipping Channel

2007· article· en· W2317926972 on OpenAlexaffabout
David Taylor, Kevin R. Hall, Neil J. MacDonald

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsQueen's University
Fundersnot available
KeywordsChannel (broadcasting)Sediment transportSedimentGeologyRange (aeronautics)Marine engineeringDrawdown (hydrology)Geotechnical engineeringGeomorphologyEngineeringTelecommunicationsAerospace engineering

Abstract

fetched live from OpenAlex

Deep draft ships transiting through confined channels can significantly alter surrounding hydrodynamic conditions. The Burlington Shipping Channel is a confined channel on Lake Ontario which has experienced significant scour due to ship drawdown. A range of investigations including the development of an SGH model of the Burlington Channel have been undertaken to understand the processes leading to scouring near the channel walls. It is proposed the direction of ship motion is fundamental to the depth of scour observed at the entrances to the channel. The SGH model achieved a good level of hydrodynamic calibration and an assessment of modeled scour based on spatial variation in critical sediment size is consistent with observed scour patterns. The critical sediment diameter along the channel is an order of magnitude greater than the native bed material. In the mid-sections of the channel, scour potential is generally independent of direction of ship motion and consecutive ship movements in opposite directions appear to cause minimal net sediment transport. The development of design criteria curves for channel protection using the SGH model is presented.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.033
GPT teacher head0.257
Teacher spread0.224 · 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

Citations0
Published2007
Admission routes2
Has abstractyes

Explore more

Same topicHydrology and Sediment Transport Processes→French-language works237,207→