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Record W4283156767 · doi:10.1080/07055900.2022.2082369

Atmospheric Observations of Weather and Climate

2022· article· en· W4283156767 on OpenAlex
Howard B. Bluestein, Frederick H. Carr, Steven J. Goodman

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueATMOSPHERE-OCEAN · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMeteorological Phenomena and Simulations
Canadian institutionsnot available
FundersNational Oceanic and Atmospheric AdministrationNational Aeronautics and Space Administration
KeywordsRadiosondeDepth soundingInstrumentation (computer programming)Mesoscale meteorologyEnvironmental scienceRemote sensingMeteorologyData assimilationLidarSatelliteRadarThunderstormAtmospheric soundingGeologyComputer scienceGeographyAerospace engineeringEngineering

Abstract

fetched live from OpenAlex

Current observation systems that provide data for the analysis and prediction of climate and day-to-day weather are described, along with plans for future systems. The basic principles of satellite, radar, lidar, and sodar measurements are summarized. Temperature and moisture measurements on planetary and synoptic scales, ranging from satellites, the radiosonde network, aircraft, and other sounding systems are described. Wind measurements from satellites, rawinsondes, air composition from satellites, the energy budget, and surface measurements are also discussed. The measuring systems for mesoscale and convective-scale weather are then noted, including satellite-borne radiation instrumentation, and lightning imaging sensors. Operational, fixed-site, and mobile and airborne research radars, surface instrumentation, and ground-based and in-situ profiling systems, aircraft-borne and shipborne instrumentation are also summarized. Special observation issues such as coordination among providers, data assimilation considerations, and data curation are then considered. Special issues for the future are noted in the last section.

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)0.000
metaresearch head score (Gemma)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0130.000

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 head0.025
GPT teacher head0.216
Teacher spread0.191 · 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