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Record W7017127641

The abandonment of the South Bay waste management area.

2018· report· en· W7017127641 on OpenAlexaboutno aff

Bibliographic record

VenueLu Zone Ul (Laurentian University) · 2018
Typereport
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Bioinformatics, and Biomedical Research
Canadian institutionsnot available
Fundersnot available
KeywordsTailingsBayLand reclamationWater qualityDrainageFishingDrainage basinNatural (archaeology)Structural basin
DOInot available

Abstract

fetched live from OpenAlex

The South Bay copper/zinc concentrator was located in the Red Lake district of Northern Ontario, and was closed in 1981.In an area of 25 hectares, 760,000 tonnes of tailings, which are composed of 41% pyrite and 4.1% pyrrhotite, are contained within low dams.The 75 hectare mine site is surrounded by recreational fishing lakes of the English river drainage system.Decommissioning procedures for the site have been developed which will, in the long term, ensure that the quality of the water leaving the waste management area is environmentally acceptable.Ecological Engineering methods provide a means to ameliorate the effects of acid mine drainage originating from the wastes on the site.In the long term, the Ecological Engineering systems implemented will be self-sustaining and maintenance free.The Ecological Engineering methods being developed are based on the results of studies carried out on the natural recovery process which takes place on abandoned tailings sites (Kalin, 1983;Kalin, 1984;Cairns, 1980), the principles of which have been presentedin detail by Kalin and van Everdingen (1988) and by Mitch and Jorgensen (1989).The metal and sulphate removal capacity of the system depends on the growth rates of the biological agents causing LIST OF TABLES 6 7 8 9 1 OA 10B 1oc 11 12 Drainage areas for tailings, Boomerang Lake and Mud Lake basin ..............

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)0.000
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.242
Threshold uncertainty score0.733

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.014
GPT teacher head0.223
Teacher spread0.209 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2018
Admission routes1
Has abstractyes

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