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Record W1987328461 · doi:10.4043/25242-ms

Reservoir Controls on the Occurrence and Production of Gas Hydrates in Nature

2014· article· en· W1987328461 on OpenAlexaboutno aff
Timothy S. Collett

Bibliographic record

VenueOffshore Technology Conference · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsnot available
FundersU.S. Bureau of Land ManagementBureau of Ocean Energy ManagementU.S. Geological SurveyU.S. Department of Energy
KeywordsClathrate hydrateSubmarine pipelineGeologyPermafrostPeninsulaHydrateContinental shelfArcticExtraction (chemistry)Structural basinOceanographyPetroleum engineeringEarth scienceGeomorphologyGeographyArchaeology

Abstract

fetched live from OpenAlex

Abstract Gas hydrates in both arctic permafrost regions and deep marine settings can occur at high concentrations in sand-dominated reservoirs, which have been the focus of gas hydrate exploration and production studies in northern Alaska and Canada, and offshore in the Gulf of Mexico, off the southeastern coast of Japan, in the Ulleung Basin off the east coast of the Korean Peninsula, and along the eastern margin of India. Production testing and modeling have shown that concentrated gas hydrate occurrences in sand reservoirs are conducive to existing well-based production technologies. Because conventional production technologies favor sand-dominated gas hydrate reservoirs, sand reservoirs are considered to be the most viable economic target for gas hydrate production and will be the focus of most future gas hydrate exploration and development projects. For both Arctic and marine hydrate-bearing sand reservoirs, it is generally accepted there are no apparent technical roadblocks to resource extraction; the remaining resource issues deal mostly with the economics of hydrate extraction.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.222
Teacher spread0.212 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2014
Admission routes1
Has abstractyes

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