{"id":"W2172078667","doi":"10.1109/tgrs.2006.885046","title":"Filament Preserving Model (FPM) Segmentation Applied to SAR Sea-Ice Imagery","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Canadian Space Agency","keywords":"Synthetic aperture radar; Segmentation; Markov random field; Artificial intelligence; Computer science; Image segmentation; Context (archaeology); Sea ice; Radar imaging; Geology; Remote sensing; Pattern recognition (psychology); Computer vision; Radar","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004314906,0.0004640686,0.0004971391,0.000578482,0.000339084,0.0004399486,0.0005424972,0.0008128944,0.0004339855],"category_scores_gemma":[0.001388364,0.0002487183,0.0006590987,0.0005803824,0.0003314868,0.0004996496,0.0004028013,0.0004408052,0.0002255352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005168787,"about_ca_system_score_gemma":0.0005313086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0058709,"about_ca_topic_score_gemma":0.008547777,"domain_scores_codex":[0.9998242,0.00003880343,0.000009676141,0.00005863844,0.00004833621,0.0000202962],"domain_scores_gemma":[0.9997048,0.0001341529,0.00004863202,0.00005680359,0.0000424955,0.0000131236],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000203611,0.00003729378,0.002314509,0.00009399426,0.00008456478,0.0003023786,0.0001602465,0.480582,0.05901604,0.004433089,0.001198715,0.4515737],"study_design_scores_gemma":[0.000003033952,0.00002261021,0.0007131675,0.000003554631,0.000009090478,0.00008043198,0.00000718804,0.9894258,0.007857399,0.001305993,0.0005659278,0.000005867441],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0505755,0.0002369195,0.94774,0.00006645474,0.00002020552,0.0000342252,0.00008015666,0.0007524666,0.0004941642],"genre_scores_gemma":[0.4659197,0.0003111433,0.5322716,0.00005501232,0.00003249397,0.00007164497,0.0003040231,0.0002208711,0.0008135281],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0058709,"threshold_uncertainty_score":0.01167345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01144879411424634,"score_gpt":0.2133418742631451,"score_spread":0.2018930801488988,"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."}}