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Record W1740955583 · doi:10.1002/ggge.20177

Observations of the volume flux of a seafloor hydrothermal plume using an acoustic imaging sonar

2013· article· en· W1740955583 on OpenAlexaboutno aff
Guangyu Xu, Darrell R. Jackson, K. G. Bemis, Peter A. Rona

Bibliographic record

VenueGeochemistry Geophysics Geosystems · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTropical and Extratropical Cyclones Research
Canadian institutionsnot available
Fundersnot available
KeywordsPlumeGeologySeafloor spreadingFlux (metallurgy)Volume (thermodynamics)Entrainment (biomusicology)GeophysicsHydrothermal ventPanacheSeabedHydrothermal circulationOceanographySeismologyMeteorologyPhysics

Abstract

fetched live from OpenAlex

We present a 26 day time series (October 2010) of physical properties (volume flux, flow velocity, expansion rate) of a vigorous deep‐sea hydrothermal plume measured using our Cabled Observatory Vent Imaging Sonar (COVIS), which is connected to the Northeast Pacific Time Series Underwater Experiment Canada Cabled Observatory at the Main Endeavour Field on the Juan de Fuca Ridge. COVIS quantitatively monitors the initial buoyant rise of the plume from to above the vents. The time series exhibits temporal variations of the plume vertical volume flux ( ), centerline vertical velocity component ( ) and expansion rate ( ); these variations have major spectral peaks at semidiurnal ( cycle/day) and inertial oscillation ( cycle/day) frequencies. The plume expansion rate (average ) is inversely proportional to the plume centerline vertical velocity component (coefficient of determination ). This inverse proportionality, as well as the semidiurnal frequency, indicates interaction between the plume and ambient ocean currents consistent with an entrainment of ambient seawater that increases with the magnitude of ambient currents. The inertial oscillations observed in the time series provide evidence for the influence of surface storms on the dynamics of hydrothermal plumes.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.298
Threshold uncertainty score1.000

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)0.0010.000

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 head0.021
GPT teacher head0.216
Teacher spread0.195 · 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.

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

Citations23
Published2013
Admission routes1
Has abstractyes

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