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Record W6955070023 · doi:10.57757/iugg23-2514

The HAWC Satellite Mission: The Canadian Contribution to NASA AOS

2023· article· en· W6955070023 on OpenAlexaffabout

Bibliographic record

VenuePublication Database GFZ (GFZ German Research Centre for Geosciences) · 2023
Typearticle
Languageen
FieldComputer Science
TopicBig Data and Digital Economy
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsWater vaporSatelliteNadirAerosolConstellationObservatoryAtmosphere (unit)Precipitation

Abstract

fetched live from OpenAlex

<!--!introduction!--> The HAWC (High-altitude Aerosols, Water vapour and Clouds) satellite mission is a highly synergistic observing system of three Canadian passive imaging sensors: the Aerosol Limb Imager (ALI) instrument, the Thin Ice Clouds in the Far InfraRed Emissions (TICFIRE), and the Spatial Heterodyne Observations of Water (SHOW) instrument. The mission was confirmed by the federal government as the Canadian contribution to NASA’s Atmospheric Observing System; a satellite constellation that will include multiple satellites with instruments to monitor aerosol, clouds, and precipitation as part of the Earth System Observatory (ESO). The HAWC instruments will work together to obtain vertically resolved measurements of aerosol and water vapour together with nadir measurements of radiation, thin ice cloud content, and cloud microphysical properties. These coordinated measurements will help build a more comprehensive understanding of climate-critical interactions of aerosol, cloud, and water vapour in the atmosphere.

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)0.003
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.066
Threshold uncertainty score0.152

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.008
Science and technology studies0.0030.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0460.017

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 head0.078
GPT teacher head0.355
Teacher spread0.277 · 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
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

Citations0
Published2023
Admission routes2
Has abstractyes

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