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Record W4414667640 · doi:10.1002/gdj3.70034

Integrated Global Radiosonde Archive Toolkit ( <scp>IGRAT</scp> ): A Python Library for Radiosonde Data Analysis

2025· article· en· W4414667640 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.

Bibliographic record

VenueGeoscience Data Journal · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsMinistry of the Environment, Conservation and ParksUniversity of Toronto
Fundersnot available
KeywordsRadiosondePython (programming language)PreprocessorSoftwareData setSet (abstract data type)

Abstract

fetched live from OpenAlex

ABSTRACT Integrated Global Radiosonde Archive Toolkit (IGRAT) is a software that allows users to process data from the Integrated Global Radiosonde Archive. The archive provides global radiosonde observations in a text‐based format that requires additional manipulation to make it suitable for analysis. IGRAT provides an easy‐to‐use set of tools to streamline this preprocessing step, allowing users to readily visualise temporal and spatial patterns, plot atmospheric profiles, and export processed data sets in the more standard formats. IGRAT is accessible through a Python library and web interface, and users can adopt it to their preferred workflow. IGRAT significantly reduces preprocessing time before analysis, making it suitable for applications in climate research, meteorology and atmospheric sciences. IGRAT is fully open‐source, allowing the community to make contributions as well as modify IGRAT for personal use.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.512
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.001
Scholarly communication0.0010.004
Open science0.0070.004
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 head0.040
GPT teacher head0.293
Teacher spread0.254 · 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