MétaCan
Menu
Back to cohort
Record W2395066187 · doi:10.1002/2016jc011744

The role of bubbles during air‐sea gas exchange

2016· article· en· W2395066187 on OpenAlexfundno aff
Steven Emerson, Seth M. Bushinsky

Bibliographic record

VenueJournal of Geophysical Research Oceans · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsnot available
FundersUniversity of Victoria
KeywordsBubbleSeawaterThermoclineEnvironmental scienceTRACERFlux (metallurgy)Atmospheric sciencesAtmosphere (unit)MeteorologyOceanographyMechanicsChemistryGeologyPhysics

Abstract

fetched live from OpenAlex

Abstract The potential for using the air‐sea exchange rate of oxygen as a tracer for net community biological production in the ocean is greatly enhanced by recent accuracy improvements for in situ measurements of oxygen on unmanned platforms. A limiting factor for determining the exchange process is evaluating the air‐sea flux contributed by bubble processes produced by breaking waves, particularly during winter months under high winds. Highly accurate measurements of noble gases (Ne, Ar & Kr) and nitrogen, N 2 , in seawater are tracers of the importance of bubble process in the surface mixed layer. We use measured distributions of these gases in the ventilated thermocline of the North Pacific and an annual time series of N 2 in the surface ocean of the NE Subarctic Pacific to evaluate four different air‐water exchange models chosen to represent the range of model interpretation of bubble processes. We find that models must have an explicit bubble mechanism to reproduce concentrations of insoluble atmospheric gases, but there are periods when they all depart from observations. The recent model of Liang et al. (2013) stems from a highly resolved model of bubble plumes and categorizes bubble mechanisms into those that are small enough to collapse and larger ones that exchange gases before they resurface, both of which are necessary to explain the data.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.483
Threshold uncertainty score0.308

Codex and Gemma teacher scores by category

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

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 head0.017
GPT teacher head0.261
Teacher spread0.245 · 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.

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

Citations67
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Geophysical Research OceansSame topicOceanographic and Atmospheric ProcessesFrench-language works237,207