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Record W4200075421 · doi:10.21105/joss.03659

argodata: An R interface to oceanographic data from the International Argo Program

2021· article· en· W4200075421 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueThe Journal of Open Source Software · 2021
Typearticle
Languageen
FieldComputer Science
TopicData Analysis with R
Canadian institutionsDalhousie UniversityBedford Institute of OceanographyFisheries and Oceans Canada
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsArgoInterface (matter)Environmental scienceOceanographyComputer scienceMeteorologyGeologyGeography

Abstract

fetched live from OpenAlex

This paper describes argodata, an R package that makes it easier to work with data acquired in the International Argo Program, which provides over two decades of oceanographic measurements from around the world. Although Argo data are publicly available in NetCDF format and several software packages are available to assist in locating and downloading relevant Argo data, the multidimensional arrays used can be difficult to understand for non-oceanographers, particulary for the expanding arrays of biogeochemical variables measured by Argo floats. Given the increasing use of Argo data in other disciplines, we built a minimal interface to the data set that uses the data frame as the primary data structure. This approach allows users to leverage the rich ecosystem of R packages that manipulate data frames (e.g., the tidyverse) and associated instructional resources.

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.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Open science
Consensus categoriesOpen science
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.431
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0030.003
Open science0.0290.012
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 head0.075
GPT teacher head0.363
Teacher spread0.287 · 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