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Record W7095558110

GEOCOMP, A NOAA AVHRR DATA GEOCODING AND COMPOSITING SYSTEM

2014· article· en· W7095558110 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSatellite Image Processing and Photogrammetry
Canadian institutionsnot available
Fundersnot available
KeywordsGeocodingCompositingRadiometric calibrationAtmospheric correctionCalibrationRadiometryIrradiance
DOInot available

Abstract

fetched live from OpenAlex

Research in the fields of global change and environmental monitoring requires timely delivery of data that is geocoded and radiometrically correct. To address this need, a system has been developed to generate near cloud-free composite images from NOAA AVHRR imagery. The system, named GEOCOMP, features many advanced capabilities including fulllO-bit processing, automated sub-pixel precision geocoding, radiometric calibration with optional atmospheric correction based on the 5S atmospheric model, and production of composite images with user specified spatial, temporal, and viewing angle restrictions. The system has been installed and is being operated by the Manitoba Remote Sensing Centre in Winnipeg, Manitoba (Canada). This paper describes the radiometric and geometric corrections performed by GEOCOMP and analyzes the accuracy of the resulting products. 1

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: Empirical · Consensus signal: none
Teacher disagreement score0.981
Threshold uncertainty score0.460

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 head0.016
GPT teacher head0.208
Teacher spread0.192 · 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
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

Citations0
Published2014
Admission routes1
Has abstractyes

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