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Record W3147189274 · doi:10.1029/2020gl091249

Marine Electromagnetic Imaging and Volumetric Estimation of Freshwater Plumes Offshore Hawai'i

2021· article· en· W3147189274 on OpenAlexaff
Eric Attias, Steven Constable, Dallas Sherman, Khaira Ismail, Christopher K. Shuler, Henrietta Dulai

Bibliographic record

VenueGeophysical Research Letters · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsFrontier Geosciences (Canada)
Fundersnot available
KeywordsSubmarine groundwater dischargeElectrical resistivity tomographyBiogeochemical cycleGeologySeafloor spreadingOceanographyWater columnPlumeSubmarine pipelineHydrogeologySalinitySeawaterEnvironmental scienceGroundwaterGeophysicsElectrical resistivity and conductivityAquiferEcology

Abstract

fetched live from OpenAlex

Abstract Submarine groundwater discharge (SGD) is an important phenomenon that governs hydrological cycles at the land‐to‐ocean transition zone. SGD manifests as cold and buoyant freshwaters influx from the seafloor to the water column that contains carbon, nutrients, metals, and greenhouse gases, altering coastal areas' oceanographical and biochemical properties. Here, we present electromagnetic imaging of large‐scale freshwater plumes in high‐resolution, offshore west Hawai'i. Electrical resistivity models detect multiple vertical freshwater plumes extending from the seafloor to the ocean surface. Additionally, our models image extensive spatially distributed surface freshwater. The resistivity of these plumes and surface freshwater ranges from ∼1 to 30 Ωm. Resistivity‐to‐salinity calculation indicates a plume‐scale salinity range of ∼0.3–9.9, containing up to 87% of freshwater. Our results imply that substantial volumes of freshwater occupy water column plumes in Hawai'i. This study offers a new and effective method to elucidate hydrogeologic and ocean processes affecting biogeochemical cycles in coastal waters worldwide.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.933
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.018
GPT teacher head0.279
Teacher spread0.261 · 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

Citations31
Published2021
Admission routes1
Has abstractyes

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