{"id":"W4294699435","doi":"10.5957/icetech-2012-171","title":"Detecting Icebergs in Sea Ice Using Dual Polarized Satellite Radar Imagery","year":2012,"lang":"en","type":"article","venue":"","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre For Cold Ocean Resources Engineering","funders":"","keywords":"Iceberg; Synthetic aperture radar; Remote sensing; Computer science; Sea ice; Satellite; Space-based radar; Radar imaging; Satellite imagery; Inverse synthetic aperture radar; Visualization; Software; Radar; Geology; Artificial intelligence; Radar engineering details; Oceanography","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":[0.001057381,0.0005066584,0.0003323421,0.00194457,0.0003091378,0.0008774892,0.0003044289,0.0003363276,0.0002543113],"category_scores_gemma":[0.001429258,0.0001794325,0.0003006955,0.0007997939,0.0003355596,0.0004918264,0.0004412384,0.0002025058,0.0001316151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002404381,"about_ca_system_score_gemma":0.0004554859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00333704,"about_ca_topic_score_gemma":0.006724318,"domain_scores_codex":[0.9996704,0.00005844943,0.00002558546,0.00008553095,0.0001009082,0.00005905658],"domain_scores_gemma":[0.9992569,0.0003023093,0.0001121999,0.00006338845,0.0001984918,0.00006670433],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00206379,0.0003763303,0.463608,0.0003846479,0.000242004,0.001094673,0.0007613131,0.08108214,0.2512368,0.001037905,0.001165532,0.1969469],"study_design_scores_gemma":[0.0000753909,0.0004724553,0.6236212,0.00006147701,0.0001939066,0.0008914841,0.0009291843,0.2728122,0.09693182,0.00151094,0.002424557,0.00007524803],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9635273,0.0001325386,0.03471817,0.00003813446,0.00001164314,0.00005353423,0.0006253903,0.0001897237,0.0007036359],"genre_scores_gemma":[0.9445003,0.0001657616,0.0529844,0.00002121459,0.00001106051,0.00002832444,0.001817488,0.00003497722,0.0004364331],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00333704,"threshold_uncertainty_score":0.006635189,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01899051757536179,"score_gpt":0.2282082836688992,"score_spread":0.2092177660935374,"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."}}