MétaCan
Menu
Back to cohort
Record W4306164068 · doi:10.3389/frsen.2022.1040835

Editorial: Remote sensing of cloud, aerosols, and radiation from satellites

2022· editorial· en· W4306164068 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

VenueFrontiers in Remote Sensing · 2022
Typeeditorial
Languageen
FieldEnvironmental Science
TopicAtmospheric aerosols and clouds
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsRemote sensingCloud computingEnvironmental scienceMeteorologyAstrobiologyComputer scienceGeographyPhysicsOperating system

Abstract

fetched live from OpenAlex

Editorial on the Research Topic Remote sensing of cloud, aerosols, and radiation from satellites Planning a research satellite mission involves a careful study phase in which science objectives are defined and the measurements necessary to achieve these objectives are identified, which then determine instrument and other mission requirements. Obtaining the necessary geophysical variables with the required accuracies necessitates suitable retrieval algorithms and methods to assess how well the objectives can be realized, all within a well-defined budget and schedule. The pre-launch objective assessment phase represents a crucial and invaluable step for defining and justifying a mission. Yet, despite their importance, these algorithms and assessments are generally not readily accessible to researchers who are not involved directly in this mission study phase. This volume aims to add some transparency to this process.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.314
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
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 head0.005
GPT teacher head0.212
Teacher spread0.207 · 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