{"id":"W4295308582","doi":"10.1109/jstars.2022.3205849","title":"Sea-Ice Mapping of RADARSAT-2 Imagery by Integrating Spatial Contexture With Textural Features","year":2022,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sea ice; Remote sensing; Computer science; Pixel; Segmentation; Artificial intelligence; Support vector machine; Random forest; Image segmentation; Robustness (evolution); Geology; Climatology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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":[0.0003552338,0.0001329167,0.0002780689,0.0001398437,0.0003077096,0.00004018445,0.0001003957,0.00005364792,0.00002667226],"category_scores_gemma":[0.00004904493,0.0001034038,0.00003521447,0.000514423,0.0000892624,0.0001051815,0.00001257991,0.0007209538,1.077311e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001664748,"about_ca_system_score_gemma":0.0001820166,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002509974,"about_ca_topic_score_gemma":0.002210336,"domain_scores_codex":[0.9987853,0.00007830565,0.0004266081,0.0001374358,0.0003682924,0.0002041079],"domain_scores_gemma":[0.9990881,0.0001750249,0.0003989341,0.00007928417,0.0002009317,0.00005774405],"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":[0.0003974309,0.00004024627,0.07439028,0.0001013046,0.0001474943,0.0001031107,0.004053615,0.01337647,0.01243442,0.00009221691,0.000554268,0.8943092],"study_design_scores_gemma":[0.002905205,0.0006897171,0.7054682,0.0004647787,0.0001248846,0.001861893,0.01251074,0.2622704,0.001793476,0.001432831,0.009749853,0.0007279888],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9779152,0.00021628,0.02039579,0.0006465209,0.0002037024,0.0001270139,0.00004222056,0.000008884032,0.0004444301],"genre_scores_gemma":[0.9294583,0.00006393151,0.06996407,0.0002487618,0.0001377243,1.298335e-8,0.00004607343,0.000005442039,0.0000756899],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8935812,"threshold_uncertainty_score":0.4216684,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01065441546139167,"score_gpt":0.1940409714603732,"score_spread":0.1833865559989815,"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."}}