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Record W1602630920

Reviews on Natural Resources in the Arctic

2001· article· en· W1602630920 on OpenAlexaboutno aff
Jong-Ryeol Yoon, Yeadong Kim

Bibliographic record

VenueOcean and Polar Research · 2001
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological Studies and Exploration
Canadian institutionsnot available
Fundersnot available
KeywordsArcticGeologyPermafrostContinental shelfOceanographyNatural gasMineral resource classificationCanada BasinFossil fuelStructural basinGeochemistrySedimentary basinSedimentary rockEarth sciencePhysical geographyGeomorphologyGeography
DOInot available

Abstract

fetched live from OpenAlex

The Arctic consists of numerous sedimentary basins containing voluminous natural resources and two of the world's major oil and gas producing areas. The western Siberia Basin in the Arctic region has the largest petroliferous province with an area of 800 1,200 km and produces more than 60% of total Russian oil production. The North Slope of Alaska produces about 20% of the U.S. output, i.e., 11% of the total U.S. consumption. Being small compared to those regions, the Canadian Northwest Territories and the Pechora Basin in Russia produce only fair amount of oil and natural gas. There are also many promising areas in the northern continental shelf of Russia. In addition to Russia, Svalbard and Greenland have been investigated for oil and gas. Gas hydrates are widespread in both permafrost regions and arctic continental shelf areas. The reserves of gas hydrates in the Arctic Ocean are about 2032% of total estimated amounts of gas hydrates in the world ocean. Mineral mining is well developed, especially in Russia. The major centers are located around the Kuznetsk Basin and Noril'sk. They are major suppliers of gold, tin, nickel, copper, platinum, cobalt, iron ore, coal as well as apatite. There are also some minings of lead-zinc in Alaska and Arctic Canada.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.389
Threshold uncertainty score0.366

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.113
GPT teacher head0.322
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 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

Citations7
Published2001
Admission routes1
Has abstractyes

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