{"id":"W7095558110","doi":"","title":"GEOCOMP, A NOAA AVHRR DATA GEOCODING AND COMPOSITING SYSTEM","year":2014,"lang":"en","type":"article","venue":"","topic":"Satellite Image Processing and Photogrammetry","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Geocoding; Compositing; Radiometric calibration; Atmospheric correction; Calibration; Radiometry; Irradiance","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008755916,0.0006371338,0.0004268452,0.002369395,0.0009602527,0.001150394,0.001031154,0.0005308122,0.009114529],"category_scores_gemma":[0.001193083,0.0003991362,0.0003341483,0.002400282,0.0004764559,0.0007565085,0.0007977677,0.000621639,0.005658813],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009246508,"about_ca_system_score_gemma":0.002809593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06912893,"about_ca_topic_score_gemma":0.1102031,"domain_scores_codex":[0.9992502,0.00005713114,0.00002766314,0.0001325648,0.0004578119,0.00007458486],"domain_scores_gemma":[0.9987528,0.00009034551,0.00008239273,0.0002121653,0.0007703613,0.00009190416],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001439719,0.0004094313,0.02798349,0.0003472704,0.0001740717,0.0004636354,0.0006706605,0.02137761,0.1539284,0.007521192,0.2363774,0.5493072],"study_design_scores_gemma":[0.0002810974,0.0002912247,0.05917561,0.00009117894,0.0001316304,0.0006135483,0.0005247731,0.2845365,0.149837,0.003514916,0.50076,0.0002423828],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1255347,0.0005330907,0.5350514,0.0005198281,0.0007119147,0.00130246,0.09288058,0.1427282,0.1007378],"genre_scores_gemma":[0.2597869,0.0002873492,0.595825,0.0003372487,0.0001672024,0.000459485,0.1075474,0.006110076,0.02947932],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.06912893,"threshold_uncertainty_score":0.1374531,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01617940500201989,"score_gpt":0.2083873897158545,"score_spread":0.1922079847138346,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}