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Record W2290388043 · doi:10.14288/1.0041981

Research on tailings in British Columbia - U.B.C. experience

2010· article· en· W2290388043 on OpenAlexaboutno aff
L. M. Lavkulich

Bibliographic record

VenuecIRcle (University of British Columbia) · 2010
Typearticle
Languageen
FieldEngineering
TopicTailings Management and Properties
Canadian institutionsnot available
Fundersnot available
KeywordsTailingsMetallurgy

Abstract

fetched live from OpenAlex

For the past several years the Department of Soil Science at the University of British Columbia has been involved in research related to vegetation of mine wastes. The general objective of the research program has been to characterize mine wastes, especially tailings, by physical, chemical, mineralogical and biological properties. In this manner, the research has been oriented towards understanding the inherent properties of mine wastes so that the materials may be transformed to a "soil" that will not only maintain vegetation but also sustain it, without the necessity of continuous management. During 1976-77 the Department of Soil Science, under the auspices of the British Columbia Department of Mines and Petroleum Resources has intensified its research on tailings. This research activity has been oriented towards a better understanding of the various types of tailings, their elemental composition, the elements they release upon weathering, growth limiting factors and vegetative success. The ultimate aim is to develop guidelines for the establishment of vegetation in the various biophysical regions of the province. It was also hoped that the mining industry would become involved in collecting the data, both environmental and laboratory, in order for them to develop their own expertise as well as help plan future reclamation programs. This integration of government, university and private sector has much to offer in terms of efficiency of utilization of resources, training and obtaining a base-line upon which British Columbia reclamation programs can be patterned.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.880
Threshold uncertainty score0.823

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.017
GPT teacher head0.206
Teacher spread0.189 · 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

Citations0
Published2010
Admission routes1
Has abstractyes

Explore more

Same venuecIRcle (University of British Columbia)Same topicTailings Management and PropertiesFrench-language works237,207