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Record W2318738430 · doi:10.1109/jstars.2014.2299813

Influence of Sand-Grain Morphology and Iron-Oxide Distribution Patterns on the Visible and Near-Infrared Reflectance of Sand-Textured Soils

2014· article· en· W2318738430 on OpenAlexaff
Gladimir V. G. Baranoski, Bradley W. Kimmel, T. Francis Chen, Erik Miranda

Bibliographic record

VenueIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAeolian processes and effects
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsWeatheringSoil waterRoundness (object)Grain sizeSphericityMineralogyIron oxideMaterials scienceParticle-size distributionGeologySoil scienceComposite materialGeochemistryMetallurgyParticle size

Abstract

fetched live from OpenAlex

The overall shape of a sand grain can be defined by two morphological properties, namely sphericity and roundness, and it is largely determined by soil-formation and weathering processes. In this paper, we investigate the effects of these properties on the visible and near-infrared reflectance of sand-textured soils characterized by the presence of iron oxides. Our investigation is supported by computer simulations performed using the SPLITS (Spectral Light Transport Model for Sand) model and considering actual sand characterization data. Our findings indicate that the influence of grain morphology may vary considerably depending on the distribution patterns of iron oxides present in sand-textured soils. These minerals may occur as pure particles, as contaminants mixed with the grain parent material, or as coatings. Since these distribution patterns are also significantly affected by soil-formation and weathering processes, we believe that the combined influence of sand-grain shape and iron-oxide distribution patterns on the reflectance of sandy landscapes should be carefully taken into account in the retrieval of information about their mineralogy and environmental history.

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 score0.676
Threshold uncertainty score0.303

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 head0.012
GPT teacher head0.218
Teacher spread0.205 · 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

Citations12
Published2014
Admission routes1
Has abstractyes

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