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Record W2011348533 · doi:10.1109/igarss.2014.6947194

Data-based disaggregation of SMOS soil moisture

2014· article· en· W2011348533 on OpenAlexafffund
Kurt C. Kornelsen, Paulin Coulibaly

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil Moisture and Remote Sensing
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaEuropean Space AgencyOntario Innovation Trust
KeywordsEnvironmental scienceRadiometerWater contentCumulative distribution functionRemote sensingMoistureSoil scienceMeteorologyProbability density functionGeologyMathematicsGeographyStatistics

Abstract

fetched live from OpenAlex

The Soil Moisture and Ocean Salinity (SMOS) microwave radiometer is used to retrieve surface soil moisture with a grid resolution of 15 km. Due to various contributing factors SMOS soil moisture is known to have bias with respect to in situ soil moisture measurements and land surface models. For this reason it is common practice to match the cumulative distribution function (CDF) of retrieved soil moisture prior to analysis. Using the concept of temporal stability this study demonstrates that CDF matching is effective for correcting the bias at both grid and sub-grid scales with minimal impact on the time in-variant component of SMOS error.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.732
Threshold uncertainty score0.308

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 head0.015
GPT teacher head0.230
Teacher spread0.215 · 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 designObservational
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
Published2014
Admission routes2
Has abstractyes

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