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Record W2018295962 · doi:10.1177/0270467609342712

Sydney Tar Ponds Remediation

2009· article· en· W2018295962 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueBulletin of Science Technology & Society · 2009
Typearticle
Languageen
FieldDecision Sciences
TopicKnowledge Management and Technology
Canadian institutionsCape Breton University
Fundersnot available
Keywordstar (computing)ChinaNova scotiaCapeEnvironmental remediationGeographyVegetation (pathology)HistoryEnvironmental scienceArchaeologyEcologyContaminationBiology

Abstract

fetched live from OpenAlex

The infamous “Sydney Tar Ponds” are well known as one of the largest toxic waste sites of Canada, due to almost 100 years of steelmaking in Sydney, a once beautiful and peaceful city located on the east side of Cape Breton Island, Nova Scotia. This article begins with a contextual overview of the Tar Ponds issue including a brief introduction and history and summaries of the effects on the earth, the people, and the biotic community (animals and vegetation). Then the authors talk about the STS analysis approach, namely, a discussion of six systems to indicate what has been brought to the earth and mankind by technology and modern industry. The remaining part of the article describes the difficulties confronting China, some of which are similar to the ones Canada faces as a result of the Tar Ponds contamination, and summarizes some of the experiences at Tar Ponds and the lessons China can learn from Tar Ponds.

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.

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)0.005
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.336
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.008
Science and technology studies0.0000.005
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.032
GPT teacher head0.325
Teacher spread0.293 · 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