{"id":"W2338225562","doi":"10.1080/07038992.2016.1152547","title":"Improved Sea Ice Concentration Estimation Through Fusing Classified SAR Imagery and AMSR-E Data","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Remote Sensing","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Sea ice; Synthetic aperture radar; Remote sensing; Pixel; Sea ice concentration; Sea ice thickness; Environmental science; Arctic ice pack; Geology; Computer science; Climatology; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003667663,0.00009615197,0.0001451458,0.0000526593,0.0002402164,0.000104829,0.0001183993,0.00005636452,0.00005456619],"category_scores_gemma":[0.0002894167,0.00006929544,0.00002404801,0.00008700282,0.0001592686,0.0009018457,0.0000063883,0.0001289094,0.000005373433],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003097628,"about_ca_system_score_gemma":0.0006242353,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02346215,"about_ca_topic_score_gemma":0.04360799,"domain_scores_codex":[0.9991519,0.00005870906,0.0002657119,0.0001467335,0.000114466,0.0002624666],"domain_scores_gemma":[0.999063,0.0001479195,0.0002292661,0.0001699073,0.0001073107,0.0002826506],"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":[0.00001680676,4.519638e-7,0.004589224,0.000009987696,0.00001592921,0.0000948895,0.0002917397,0.00004846797,0.0004166477,0.000006842615,0.0001825711,0.9943264],"study_design_scores_gemma":[0.0004947459,0.00006469417,0.01355213,0.0003119078,0.00006358868,0.001204986,0.0005296183,0.9772291,0.0001445144,0.001226285,0.004983988,0.0001944349],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5924575,0.0002534538,0.4019236,0.003580665,0.0007050557,0.00008054078,0.00007588969,0.000008001286,0.0009152986],"genre_scores_gemma":[0.8669025,0.0000870337,0.1324138,0.0003431898,0.0001864307,2.771658e-10,0.00003144212,0.000004020849,0.00003153614],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.994132,"threshold_uncertainty_score":0.9830407,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0283559701820102,"score_gpt":0.2296203472886483,"score_spread":0.2012643771066381,"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."}}