MétaCan
Menu
Back to cohort
Record W2369320924

Modeling Envisat-1 Dual-Polarized Data and Its Applications

2006· article· en· W2369320924 on OpenAlexaboutno aff
Zhen Li, Xin Ren

Bibliographic record

VenueNational Remote Sensing Bulletin · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil Moisture and Remote Sensing
Canadian institutionsnot available
Fundersnot available
KeywordsRemote sensingRadarSurface roughnessSurface finishBackscatter (email)Dual (grammatical number)Environmental scienceGeologyComputer scienceMaterials science
DOInot available

Abstract

fetched live from OpenAlex

Bare surface co-polarized backscattering model and roughness computing model is established using AIEM simulated data,based on the configuration and dual-polarized nature of Envisat-1 ASAR data.The former expresses co-polarized backscattering coefficient as the function of incidence angle and two surface parameters(namely,soil moisture and roughness);and the later gives the method to obtain roughness using dual-polarized radar data.Soil moisture is estimated by combining the two models,and validation is performed by both simulated data and in-situ data.The results show that the two models are reliable and useful.The foundation of dual-polarized model will benefit to the modeling and applications of multi-polarized radar data on PALSAR(Japan) and RADARSAT-2(Canada) in the future.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.754
Threshold uncertainty score0.635

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)0.0000.000

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 head0.022
GPT teacher head0.252
Teacher spread0.230 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations3
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueNational Remote Sensing BulletinSame topicSoil Moisture and Remote SensingFrench-language works237,207