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Record W4244476932 · doi:10.4095/219686

Radiative Transfer Codes Applied to Hyperspectral Data for the Retrieval of Surface Reflectance

2000· report· en· W4244476932 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEngineering
TopicInfrared Target Detection Methodologies
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsHyperspectral imagingRadiative transferReflectivityRemote sensingEnvironmental scienceAtmospheric radiative transfer codesSurface (topology)GeographyOpticsPhysicsMathematicsGeometry

Abstract

fetched live from OpenAlex

The present investigation evaluates surface reflectance retrieved from Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Compact Airborne Spectrographic Imager (casiTM) data using the atmospheric radiative transfer (RT) codes: ATREM, CAM5S, and MODTRAN4. The retrieved surface reflectances were compared with ground-based reflectances acquired with a GER3700TM spectroradiometer for a playa and canola target. The results showed that the best overall performance was achieved with MODTRAN4, followed by ATREM and CAM5S. Major differences occur in the stronger gas absorption regions. At wavelengths unaffected by strong gaseous absorption, the performance was similar for the three RT codes even though ATREM and CAM5S have faster execution times.

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.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.595
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.152
GPT teacher head0.356
Teacher spread0.204 · 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

Quick stats

Citations2
Published2000
Admission routes1
Has abstractyes

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