{"id":"W2111829195","doi":"10.1109/igarss.1996.516527","title":"Temporal mixture analysis of SMMR sea ice concentrations","year":2002,"lang":"en","type":"article","venue":"","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Endmember; Sea ice; Remote sensing; Geology; Northern Hemisphere; Spectral analysis; Oceanography; Climatology; Physics; Hyperspectral imaging","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003466472,0.00006494531,0.0001262804,0.000114816,0.00001949408,0.00001708283,0.00005427398,0.00004698617,0.0004280836],"category_scores_gemma":[0.00002281681,0.00006275138,0.00006680004,0.0006806625,0.00002536997,0.000082596,0.000003828183,0.00005632586,0.00004058161],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002907863,"about_ca_system_score_gemma":0.000002813378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002566435,"about_ca_topic_score_gemma":0.00006680084,"domain_scores_codex":[0.9995484,0.0000123256,0.0001657487,0.00008600595,0.00009601063,0.00009155535],"domain_scores_gemma":[0.9996327,0.00003048221,0.00002547408,0.000220919,0.00005603995,0.0000344252],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000533154,0.0001994141,0.05095064,0.0001316658,0.003109274,0.00001436897,0.002402379,0.3557559,0.4357319,0.003160932,0.1214753,0.02706283],"study_design_scores_gemma":[0.00006095633,0.000004211748,0.01198409,0.000002978545,0.0001897444,6.959313e-7,0.0000351428,0.9745688,0.009637728,0.00000555691,0.003437107,0.00007300296],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5058926,0.0005863941,0.3087149,0.001336465,0.000357604,0.000291575,0.00005242606,0.0009448443,0.1818231],"genre_scores_gemma":[0.9930023,0.00002197491,0.005845637,0.00002475866,0.00001995836,5.87045e-7,0.00003697011,0.000009459993,0.001038347],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6188129,"threshold_uncertainty_score":0.4687215,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02064398993078247,"score_gpt":0.2202085765302915,"score_spread":0.199564586599509,"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."}}