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Record W2605952747 · doi:10.1080/14634988.2017.1316658

Environmental monitoring to guide and assess the effectiveness of Randle Reef sediment remediation on the recovery of Hamilton Harbour

2017· article· en· W2605952747 on OpenAlexaff
Matthew Graham, Erin Hartman, Rupert Joyner, Kay Kim, Roger Santiago

Bibliographic record

VenueAquatic Ecosystem Health & Management · 2017
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsEnvironmental remediationHarbourEnvironmental scienceReefSedimentRemedial actionEnvironmental impact assessmentEnvironmental engineeringContaminationOceanographyGeologyEcology

Abstract

fetched live from OpenAlex

Randle Reef is a 60 hectare portion of the Hamilton Harbour bed, heavily contaminated with polycyclic aromatic hydrocarbons and heavy metals. Remediation of contaminated sediment at Randle Reef is currently underway and is expected to be completed by 2022. In order to measure the effectiveness of the remedial effort on the surrounding ecosystem as well as enable the project's success to be critically evaluated, short and long term site-specific monitoring studies are required. As such, research scientists and sediment remediation specialists have collaborated to develop a site-specific, comprehensive series of environmental monitoring plans. The monitoring plans use several metrics to determine the state of the ecosystem prior to, during and post remediation. Monitoring studies have been designed to measure physical, biological and chemical trends over time. These studies will be used to determine the overall effectiveness of the remediation project and ultimately lead to the eventual delisting of Hamilton Harbour from the list of Great Lakes Areas of Concern. This article is a synopsis of the environmental monitoring studies that have been designed to guide and assess the effectiveness of the Randle Reef Sediment Remediation Project.

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)0.004
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.785
Threshold uncertainty score0.422

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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 head0.018
GPT teacher head0.251
Teacher spread0.233 · 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 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

Citations4
Published2017
Admission routes1
Has abstractyes

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