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A model for technology development in oil sands tailings

2013· article· en· W2887149332 on OpenAlexaboutno aff
Iain Gidley, Jeremy Boswell

Bibliographic record

VenuePaste/˜Pœaste · 2013
Typearticle
Languageen
FieldEngineering
TopicMining Techniques and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsTailingsOil sandsProcess (computing)Resource (disambiguation)Technology developmentNew product developmentBusinessRisk analysis (engineering)EngineeringComputer scienceManufacturing engineeringMarketing

Abstract

fetched live from OpenAlex

Technology development is a key component of oil sands tailings management. As new needs arise from production demands, increasing environmental standards, heightened regulatory vigilance and cost saving imperatives, technology must be developed to meet those needs. The development of tailings technology has become a time- and resource-intensive process, with no guarantees that the investment will lead to a commercially viable product. The demand for a robust technology development process was recognised as key to the development of Tailings Development Roadmaps for the Alberta Innovates – Energy and Environment Solutions (AI-EES) project. One of the key components of the project was to deliver a tailings technology development model that would serve as a guideline for technology development within the oil sands tailings industry. In providing a framework for technology development, valuable resources are carefully integrated so as to enhance the prospects for success, while reducing risks and delays. This paper describes an 18 step iterative technology development model, developed through a literature review of technology development within international mining and other industries and tailings sectors, consultation with oil sands tailings industry experts, and a specialist workshop. The 18 steps are integrated into four stages of development: Formulation and Mobilisation; Research; Development; Commercial Implementation. Each stage and each step is defined in terms of priorities, goals, pitfalls, roadblocks and remedies along the technology development path, with essential iterations and linkages to other steps. The ultimate goal of the model is to reduce the number of promising tailings technologies that fail, to identify potential fatal flaws as early as possible in the development cycle, and to focus the investment of time, funding and valuable resources on the most rewarding technologies. In presenting this model, it is the authors’ hope that other tailings sectors may benefit from the insights gained in the oil sands, to provide scrutiny of the model proposed, and offer additional learning which may add further value to tailings technology development worldwide, and particularly in the paste tailings community.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.802
Threshold uncertainty score0.600

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 studies0.0000.000
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 head0.017
GPT teacher head0.201
Teacher spread0.184 · 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 designSimulation or modeling
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
Published2013
Admission routes1
Has abstractyes

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