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Record W1995197227 · doi:10.1029/01eo00004

Software simplifies air‐sea data estimates

2001· article· en· W1995197227 on OpenAlexaff
Rich Pawlowicz, Bob Beardsley, Steve Lentz, Ed Dever, Ayal Anis

Bibliographic record

VenueEos · 2001
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMeteorological Phenomena and Simulations
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAtmosphere (unit)Coupling (piping)SoftwareBoundary (topology)Computer scienceMeteorologyFlux (metallurgy)Environmental scienceBoundary layerWork (physics)ClimatologyGeologyGeographyAerospace engineeringMathematicsEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

The atmosphere and oceans interact at the ocean surface through boundary layers that are millimeters to many tens of meters thick. Processes at work in this relatively thin region are crucial in controlling the coupling between air and ocean and, as such, are important both in studies of the ocean or atmosphere in isolation, and in studies of the coupled system that investigate—for example—interannual climatic variability However, as a practical matter, attempts to generate flux estimates from particular observational data sets often involve a great deal of effort, since the relevant parameterizations are scattered throughout the literature; may have only limited applicability to certain locations and regimes; and are found using algorithms that are often complex and iterative. This can be especially frustrating when boundary layer theory is only peripheral to the main scientific or educational interest.

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)0.002
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Software · Consensus signal: none
Teacher disagreement score0.050
Threshold uncertainty score0.169

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0500.030

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 head0.074
GPT teacher head0.275
Teacher spread0.201 · 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 designNot applicable
Domainnot available
GenreSoftware

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

Citations41
Published2001
Admission routes1
Has abstractyes

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