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Record W2901795205 · doi:10.2166/wqrj.2018.020

A comparative analysis of practitioners' experience in sediment remediation projects to highlight best practices

2018· article· en· W2901795205 on OpenAlexaff
Zobia Jawed, Gail Krantzberg

Bibliographic record

VenueWater Quality Research Journal · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental Justice and Health Disparities
Canadian institutionsMcMaster University
Fundersnot available
KeywordsEnvironmental remediationStakeholderEnvironmental planningBest practiceSedimentEnvironmental resource managementPolitical scienceEnvironmental sciencePublic relationsGeologyContamination

Abstract

fetched live from OpenAlex

Abstract The Randle Reef contaminated site, located in the southwest corner of Hamilton Harbour, is approximately 60 hectares in size. This site contains approximately 695,000 m3 of sediment contaminated with polycyclic aromatic hydrocarbons (PAHs) and metals. The complex Randle Reef sediment remediation project is finally coming to fruition after more than 30 years of study, discussion, collaborations, stakeholder consensus-building, and debate. This paper unravels the reasons behind the delays associated with implementing sediment management at the Randle Reef site. In-depth interviews with experts and professionals from organizations who are/were involved in the project were conducted to identify the nature of performance in five theme areas that are important for successful action namely: (1) participation of appropriate actors with common objectives; (2) funding and resources; (3) decision-making process; (4) research and technology development; and (5) public and political support. It is evident from this study that the hurdles to progress with addressing contaminated sediment sites involve technical, political, regulatory as well as social challenges. We offer potential solutions and a series of recommendations based on experts' first-hand experience with the management of such complex sites to inform how future remediation projects can overcome obstacles. This article has been made Open Access thanks to the kind support of CAWQ/ACQE (https://www.cawq.ca).

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)0.010
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.099
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.450
GPT teacher head0.581
Teacher spread0.130 · 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 designQualitative
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

Citations4
Published2018
Admission routes1
Has abstractyes

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