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Record W3094069698 · doi:10.1002/rem.21670

Review of remedial options for the Boat Harbour remediation project in Nova Scotia, Canada

2020· article· en· W3094069698 on OpenAlexaffabout
Lyndsay Eichinger, Tony R. ‎Walker

Bibliographic record

VenueRemediation Journal · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental Justice and Health Disparities
Canadian institutionsDalhousie University
Fundersnot available
KeywordsEnvironmental remediationEffluentDredgingHarbourEnvironmental scienceEstuaryNova scotiaWastewaterRecreationWaste managementEnvironmental engineeringEnvironmental protectionContaminationEngineeringFisheryGeographyOceanographyEcologyArchaeology

Abstract

fetched live from OpenAlex

Abstract Boat Harbour, located in Pictou County, Nova Scotia, Canada has been receiving industrial effluent wastewater from a nearby kraft pulp mill and chlor‐alkali plant for over 50 years. Before receiving industrial effluent wastewater, the tidal estuary was culturally significant to the nearby Pictou Landing First Nation community. The tidal estuary was known for its medicinal, recreational, ceremonial, and subsistence functions. Formally a 140‐ha natural tidal estuary, raw industrial wastewater was discharged into Boat Harbour beginning in 1967. Since inception, effluent treatment has undergone several upgrades in aeration capacity within the Boat Harbour Effluent Treatment Facility (BHETF) until the cessation of effluent discharge in 2020. Fifty years of industrial wastewater effluent discharge has resulted in widespread inorganic and organic contamination of unconsolidated sediments and surface water. Primary contaminants of concern include metals, dioxins and furans, and polycyclic aromatic hydrocarbons. The province of Nova Scotia has committed to the remediation of the BHETF, estimated to cost over $292 million CAD. The goal of the remediation program is to return Boat Harbour to its natural state as a tidal estuary to restore the historical, traditional, and recreational uses of the land. Remediation components and alternatives were rated based on technical (26%), environmental (24%), economic (22%), social (14%), and regulatory (14%) weighted indicators. Criteria weighting for the five indicator categories was determined collaboratively with stakeholders. For each design component of remediation, a list of approaches was developed along with subsequent alternatives. Approaches and alternatives were screened to eliminate options that were not technically feasible or did not align with remediation goals. The remaining feasible concepts underwent detailed review and evaluation to select Qualified Remedial Options to be shared with stakeholders for input.

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)0.002
metaresearch head score (Gemma)0.005
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: Review · Consensus signal: Review
Teacher disagreement score0.279
Threshold uncertainty score0.562

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.010
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.066
GPT teacher head0.347
Teacher spread0.281 · 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
GenreReview

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

Citations5
Published2020
Admission routes2
Has abstractyes

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