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Record W2003764813 · doi:10.1029/2006eo220001

Shallow seabed methane gas could pose coastal hazard

2006· article· en· W2003764813 on OpenAlexafffund
Angus I. Best, Michael D. Richardson, Bernard P. Boudreau, Alan Judd, Ira Leifer, Anthony P. Lyons, Christopher S. Martens, Danial L. Orange, Simon J. Wheeler

Bibliographic record

VenueEos · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsDalhousie University
FundersOffice of Naval ResearchDalhousie University
KeywordsSeabedBiogeochemical cycleSeafloor spreadingMethaneContinental marginOceanographyGeologySedimentContinental shelfOrganic matterEnvironmental sciencePhytodetritusEstuaryEarth scienceGeomorphologyEcologyBenthic zonePaleontology

Abstract

fetched live from OpenAlex

Abnormally high levels of methane gas in seafloor sediments could pose a major hazard to coastal populations within the next 100 years through their impact on climate change and sea level rise. Marine scientists have known for many years that biogenic methane (CH4) is generated in shallow seabed sediments on continental margins, especially in rapidly deposited muddy sediments with high organic matter content (see Methane Flux Control in Ocean Margin Sediments (METROL) project in Mienert et al., [2004]). Gassy sediments are found in river deltas, estuaries, and harbors, but also in deeper waters on continental shelves and slopes. Human activities can accelerate natural seafloor gas generation by increasing the supply of sediments and organic matter from rivers through deforestation and intensive farming, and also by the disposal of human waste at sea. When this extra organic matter becomes buried to about one meter beneath the seabed, biogeochemical processes start to convert it to CH4 [Floodgate and Judd, 1992]. The impact of this extra CH4 could be felt within the next 100 years, assuming a one‐centimeter‐per‐year sediment accumulation rate.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.027

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 communication0.0010.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0080.001

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 head0.008
GPT teacher head0.213
Teacher spread0.205 · 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

Citations57
Published2006
Admission routes2
Has abstractyes

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