{"id":"W1874818524","doi":"10.5194/tc-10-401-2016","title":"Late-summer sea ice segmentation with multi-polarisation SAR features in C and X band","year":2016,"lang":"en","type":"article","venue":"The cryosphere","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Science Council; Framsenteret; Norsk Polarinstitutt; Rural Development Administration; University of Alberta","keywords":"Geology; Sea ice; Synthetic aperture radar; Remote sensing; Segmentation; Satellite; Feature (linguistics); Geodesy; Brightness; Artificial intelligence; Climatology; Computer science; Physics; Optics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001558742,0.0001042736,0.00008743875,0.00001254875,0.0001370802,0.00003850892,0.00008237662,0.00005586076,0.0004650364],"category_scores_gemma":[0.000008012736,0.0000510118,0.0000142432,0.00009628771,0.0001025062,0.0002311547,0.000006272434,0.00009852963,0.00005067039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007950988,"about_ca_system_score_gemma":0.00002628754,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004653158,"about_ca_topic_score_gemma":0.04702305,"domain_scores_codex":[0.9993376,0.00005754535,0.0001053997,0.0001740447,0.0001412839,0.0001841682],"domain_scores_gemma":[0.9996324,0.0001361222,0.00005489843,0.0001070949,0.00001916771,0.00005028646],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006295468,0.000008876919,0.9486585,0.000009350483,0.00001123418,0.000004925319,0.000750051,0.0002846181,0.0000816809,0.00001167018,0.0001160959,0.05],"study_design_scores_gemma":[0.0007219442,0.00006588508,0.993889,0.00003688701,0.00001340399,0.00001273845,0.0008032467,0.003269064,0.00004458611,0.0002113701,0.0008132013,0.000118713],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.995445,0.0002413863,0.002309314,0.0007459223,0.0000797676,0.0001863958,0.00004379322,0.00001924999,0.0009291816],"genre_scores_gemma":[0.9964724,0.0001107763,0.001577844,0.0003072036,0.00004328117,9.162098e-7,0.00003592749,0.000004259613,0.001447361],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04988128,"threshold_uncertainty_score":0.9703663,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009405877357239124,"score_gpt":0.2086814738746075,"score_spread":0.1992755965173683,"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."}}