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Record W2937584739 · doi:10.3390/jmse7040106

An Investigation into the Suitability of GGBS and OPC as Low Percentage Single-Component Binders for the Stabilisation and Solidification of Harbour Dredge Material Mildly Contaminated with Metals

2019· article· en· W2937584739 on OpenAlexaff
Michael O’Shea, Kim Gray, Jimmy Murphy

Bibliographic record

VenueJournal of Marine Science and Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsStantec (Canada)
Fundersnot available
KeywordsDredgingHarbourContaminationEnvironmental scienceEnvironmental remediationWaste managementLeaching (pedology)Hazardous wastePort (circuit theory)Environmental engineeringEngineeringGeology

Abstract

fetched live from OpenAlex

The occurrence of contaminated materials encountered during harbour dredging is becoming increasingly problematic for harbour and port authorities. The risks to human health, wildlife and port infrastructure of exposure to such contaminants necessitates the removal or containment of such risks. As with contaminated terrestrial sites the solidification and stabilization (S/S) of this material has been proven to be an effective alternative to disposing of contaminants off-site, typically via dumping at sea or in a landfill. Research, to date, on S/S has been focused on heavily contaminated sediments in large industrial ports. However, with tightening environmental regulations, the limits of acceptable contamination are generally decreasing. This means the number of port dredging projects requiring contaminant remediation is increasing. There is now a need to examine the effectiveness of S/S on harbour sites that are mildly contaminated from both an environmental and project feasibility viewpoint. To that end, this study examines the effectiveness of various S/S mix percentages of ordinary Portland cement (OPC) and ground granulated blast furnace slag (GGBS) in retarding the leaching of contaminants from a mildly contaminated harbour site.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.373
Threshold uncertainty score0.174

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.012
GPT teacher head0.226
Teacher spread0.214 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2019
Admission routes1
Has abstractyes

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