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Record W2138201198 · doi:10.1109/igarss.1989.577790

The Effect Of Agricultural Parameters On Radar Cross-section A Linear Regression Model

2005· article· en· W2138201198 on OpenAlexaffabout
G. C. Hussey, G. J. Sofko, B. Brkco, J. A. Koehler, M. McKibben

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil Moisture and Remote Sensing
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsEnvironmental scienceLinear regressionTopsoilRadarWater contentCorrelation coefficientRegression analysisGrowing seasonSoil scienceRemote sensingHydrology (agriculture)AgronomyMathematicsStatisticsGeographySoil waterGeologyEngineering

Abstract

fetched live from OpenAlex

During the entire growing season of 1987, FM microwave scatterorneter measurements in three frequency bands (L, C, and Ku), at ten incidence angles and at four polarization modes (HH, HV, VH, VV) were acquired in Saskatchewan for agricultural test plots of wheat. Concurrently with the radar measurements, both crop and soil data were collected. For identification of the relative importance of the agricultural parameters upon the radar signal, a linear correlation model between the differential scattering coefficient 0° and five agricultural parameters (topsoil and subsoil moisture, plant water content, dry green biomass, and leaf area index) has been developed. In this paper the regression model results are presented.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.381
Threshold uncertainty score0.139

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 head0.008
GPT teacher head0.249
Teacher spread0.241 · 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 designBench or experimental
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
Published2005
Admission routes2
Has abstractyes

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