MétaCan
Menu
← Back to cohort
Record W4366605798 · doi:10.1002/9781119828242.ch8

Satellite Sensors for Sea Ice Monitoring

2023· other· en· W4366605798 on OpenAlexaff
Mohammed Shokr, Nirmal K. Sinha

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsNational Research Council CanadaEnvironment and Climate Change Canada
Fundersnot available
KeywordsRemote sensingSea iceAltimeterSatelliteSynthetic aperture radarScatterometerRadar altimeterScale (ratio)Space-based radarGeologyMeteorologyRadarRadar imagingComputer scienceGeographyRadar engineering detailsWind speedCartographyEngineeringAerospace engineering

Abstract

fetched live from OpenAlex

This chapter presents a brief account on satellite remote sensors used to study and monitor sea ice, particularly in the polar regions. The material covers sensors that provide information at three spatial scales: synoptic scale covering the entire polar region, regional scale (hundreds or thousands of kilometers coverage), and tactical scale (at fine resolution of tens or hundreds of meters), which is mainly offered by synthetic aperture radar (SAR). The chapter covers all categories of optical, thermal infrared, passive microwave (PM), SAR, scatterometer and altimeter systems. It presents technical details about the sensors and, in some cases, examples of the products from the data. The data are used to retrieve key sea ice parameters. Retrieval methods are presented for surface information and ice geophysical parameters, respectively.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.035
Threshold uncertainty score0.119

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0350.030

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 head0.020
GPT teacher head0.237
Teacher spread0.217 · 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
GenreOther

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 routes1
Has abstractyes

Explore more

Same topicArctic and Antarctic ice dynamics→French-language works237,207→