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Record W2386898610

World Oil Shale Utilization and Its Future

2006· article· en· W2386898610 on OpenAlexaboutno aff
Shuyuan Li

Bibliographic record

VenueJournal of Jilin University · 2006
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsnot available
Fundersnot available
KeywordsOil shaleShale oil extractionRetortShale oilTight oilUnconventional oilShell in situ conversion processOil shale gasSynthetic crudeOil reservesChinaPetroleum industryFossil fuelNatural resource economicsPetroleum engineeringEnvironmental scienceGeologyPetroleumWaste managementGeographyEngineeringEnvironmental engineeringArchaeologyPaleontologyEconomics
DOInot available

Abstract

fetched live from OpenAlex

Oil shale resources are abundant in the world.The in-place shale oil resources,accounted from the world proven oil shale reserves,are much larger than the world crude oil proven reserves.US has the most abundant oil shale reserves in the world,the followings are: Russia,Zaire,Brazil,Canada,Jordan,Australia and China.The oil shale retorting for producing shale oil started since the first half of the nineteenth century in Western European countries.First retorting built in France,then England,Germany,Spain,etc.Hereafter,due to the development of cheaper crude oil,the shale oil production decreased.However,shale oil industry became active again due to the world oil crisis.(During) the history of more than one hundred years,oil shale industry waved several times.Today,China,Estonia and Brazil have their shale oil commercial production.Estonia,Germany,China and Israel have the oil shale combustion enterprises for producing steam and power.Since the recent higher world crude oil price,the shale oil production in many countries becomes profitable.China,Estonia and some other countries are planning to enlarge the shale oil production;Mongolia,Jordan,etc.,are considering to build up shale oil plant;US intended to utilize his plentiful oil shale resources.

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 score0.523
Threshold uncertainty score0.174

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 head0.011
GPT teacher head0.187
Teacher spread0.176 · 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

Citations21
Published2006
Admission routes1
Has abstractyes

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