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Record W1974493306 · doi:10.1029/2000gl011585

The Assessment of Marine Gas Hydrates Through Electrical Remote Sounding: Hydrate Without a BSR?

2000· article· en· W1974493306 on OpenAlexafffundabout
Jian Yuan, R. N. Edwards

Bibliographic record

VenueGeophysical Research Letters · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsClathrate hydrateGeologySedimentHydrateSubmarine pipelineElectrical resistivity and conductivityMineralogyDrillingPetrologyGeomorphologyOceanographyMaterials science

Abstract

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Marine gas hydrates, prevalent in offshore sediments in Canada and Japan, are a possible hydrocarbon resource, a hazard to drilling and a source of a major greenhouse gas. Quantitative estimates of hydrate concentrations in deep sea sediment are difficult to obtain by conventional methods. We have sought novel techniques specifically designed for assessment such as transient electric dipole‐dipole electromagnetics. The latter method is based on the assumption of a reduced electrical conductivity in hydrate rich zones. Field trails of new apparatus on the Cascadia margin have proven successful. Excellent data were collected and analysed using a differential phase method to reduce systematic error. Apparent resistivities collected on three lines demonstrate that the resistivity of the seafloor is remarkably uniform over the whole survey area. The average hydrate concentration, deduced with the aid of a reference model based on the electrical logs of ODP holes 888 and 889, is about 17–26% of pore space (9–13% of sediment volume) in the 100 m interval above the BSR. The values are consistent with those obtained by other analyses. Further, the presence of hydrate is predicted in a region to the east of 889 B where there is no visible BSR.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.704
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.025
GPT teacher head0.325
Teacher spread0.300 · 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; both teacher heads agree on what is shown here.

Study designBench or experimental
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

Citations122
Published2000
Admission routes3
Has abstractyes

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