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Record W1970296706 · doi:10.1061/9780784413067.008

Randle Reef Sediment Remediation Project

2013· article· en· W1970296706 on OpenAlexaff
Barry L. Kellems, Kristof Fabian, Barbara Orchard, Rhiannon Parmelee, Brooke Bonkoski, Philip Spadaro, Jamie Beaver, William P. Fitzgerald, Roger Santiago, Wally Rozenberg

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMarine and Offshore Engineering Studies
Canadian institutionsHamilton Regional Laboratory Medicine ProgramEnvironment and Climate Change CanadaMinistry of the Environment, Conservation and Parks
Fundersnot available
KeywordsDredgingEnvironmental remediationEnvironmental scienceSedimentContainment (computer programming)ReefWaste managementCoal tarEnvironmental engineeringtar (computing)ContaminationMining engineeringCoalGeologyEngineeringOceanography

Abstract

fetched live from OpenAlex

The Randle Reef site is the largest coal tar-contaminated sediment site in the Great Lakes. This paper presents a summary of the design for the Randle Reef Remediation Project, which involves dredging 500,000 cubic meters (654,000 cubic yards) of sediment and construction of a 7.5-hectare (18.5-acre) Engineered Containment Facility (ECF) for containment of the dredged material. Contaminants of concern are polycyclic aromatic hydrocarbons (PAHs), as well as metals and coal-tar nonaqueous-phase liquid (NAPL). The ECF will serve to isolate contaminated sediment beneath the containment facility and dredged from the Harbour and will provide new land for a near-shore island terminal and provide berths for deep draft vessels. A multi-layer cap will be constructed over the dredged material placed in the ECF. The remediation project includes many design elements which are summarized in this paper. Construction is anticipated to begin in 2014/15.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.070
Threshold uncertainty score0.138

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.004

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 head0.007
GPT teacher head0.188
Teacher spread0.182 · 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 designNot applicable
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

Citations3
Published2013
Admission routes1
Has abstractyes

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