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Record W2141656898 · doi:10.1109/tgrs.2011.2173940

A Monte Carlo Method for Simulating Scattering From Sea Ice Using FVTD

2011· article· en· W2141656898 on OpenAlexaff
Dustin Isleifson, Ian Jeffrey, L. Shafai, Joe LoVetri, David G. Barber

Bibliographic record

VenueIEEE Transactions on Geoscience and Remote Sensing · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMonte Carlo methodScatteringScatterometerSea iceGaussianRemote sensingStatistical physicsComputational physicsMeteorologyGeologyPhysicsOpticsMathematicsStatisticsWind speed

Abstract

fetched live from OpenAlex

A scattering model based on a Monte Carlo method and the finite-volume time-domain (FVTD) method has been created for sea ice scattering simulations. Statistical methods were used to generate a Gaussian-distributed randomly rough surface. The Polder-Van Santen-de Loor (PVD) model was used to estimate the sea ice dielectric values with inputs based upon actual measured physical variables obtained during field-based experiments and well-known parameterizations. Scattering simulations were performed through an application of the scattered-field (SF) formulation invoked in an FVTD computational engine. Simulated SFs were compared with C-band scatterometer measurements and showed good agreement for copolarized signals in a series of case studies. The developed simulation method has the potential to be used for a variety of sea ice types under different physical conditions.

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: Methods · Consensus signal: none
Teacher disagreement score0.962
Threshold uncertainty score0.966

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 studies0.0010.000
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 head0.040
GPT teacher head0.259
Teacher spread0.219 · 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
GenreMethods

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

Citations17
Published2011
Admission routes1
Has abstractyes

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