{"id":"W2098297495","doi":"10.1109/igarss.2009.5417991","title":"Investigation of Radarsat-2 and Terrasar-X data for river ice classification","year":2009,"lang":"en","type":"preprint","venue":"","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Remote sensing; Polarimetry; Support vector machine; Radar; C band; Synthetic aperture radar; Dual-polarization interferometry; Cover (algebra); Sea ice; Computer science; Geology; Meteorology; Geography; Artificial intelligence; Antenna (radio); Physics; Telecommunications; Engineering; Scattering; Optics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003992735,0.0004747273,0.000379273,0.001900846,0.0002244827,0.000832453,0.0004373795,0.0004235265,0.0005439882],"category_scores_gemma":[0.005840418,0.0001783204,0.0003211704,0.001383114,0.0002046575,0.001079734,0.0003312979,0.0003865722,0.0003054258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003005453,"about_ca_system_score_gemma":0.0003395865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002971147,"about_ca_topic_score_gemma":0.003144148,"domain_scores_codex":[0.9983828,0.000714946,0.00009005582,0.0001671055,0.0005437259,0.0001013326],"domain_scores_gemma":[0.9953975,0.002591741,0.0003742937,0.000323956,0.001153337,0.0001591859],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002747247,0.001434696,0.4142172,0.0005947835,0.0004494022,0.0009609605,0.0005412109,0.1044379,0.04913509,0.00257597,0.005067162,0.4178383],"study_design_scores_gemma":[0.0001892099,0.001049544,0.3218739,0.00009619207,0.0001812077,0.0004624905,0.0008581272,0.6266458,0.04292089,0.001101692,0.004554453,0.0000664336],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9852094,0.0004191136,0.009928837,0.0002428525,0.00003723973,0.00007359107,0.0008065878,0.0002212408,0.003061172],"genre_scores_gemma":[0.9821991,0.0002334277,0.01468123,0.0000517024,0.00002821189,0.00002638294,0.002185177,0.00003713643,0.0005576042],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003992735,"threshold_uncertainty_score":0.02111584,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08727705912651691,"score_gpt":0.2640942909007094,"score_spread":0.1768172317741925,"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."}}