{"id":"W2223430424","doi":"10.3394/0380-1330(2007)33[722:ssrsog]2.0.co;2","title":"Satellite SAR Remote Sensing of Great Lakes Ice Cover, Part 1. Ice Backscatter Signatures at C Band","year":2007,"lang":"en","type":"article","venue":"Journal of Great Lakes Research","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":58,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Jet Propulsion Laboratory; National Oceanic and Atmospheric Administration","keywords":"Remote sensing; Sea ice; Backscatter (email); Synthetic aperture radar; Scatterometer; Geology; Radar; Snow; Sea ice thickness; Polarimetry; Satellite; Open water; Sea ice concentration; Arctic ice pack; Environmental science; Meteorology; Wind speed; Oceanography; Geography; Geomorphology; Scattering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004208068,0.0002354579,0.0004891268,0.0004242613,0.0004172037,0.00009353171,0.0004500995,0.0002106803,0.002131189],"category_scores_gemma":[0.0003701168,0.0001710395,0.0002233543,0.0005678075,0.0005517351,0.0003958169,0.00005051516,0.0009987318,0.0001594862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005737193,"about_ca_system_score_gemma":0.0001731134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005443987,"about_ca_topic_score_gemma":0.002316046,"domain_scores_codex":[0.9960285,0.0003752232,0.000785188,0.0002909319,0.001660635,0.0008595529],"domain_scores_gemma":[0.996365,0.00181394,0.0003660186,0.0003244427,0.0007371003,0.0003935459],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.005330353,0.00009862588,0.489302,0.0005409095,0.0005552121,0.003203759,0.003792101,0.002240916,0.007592325,0.00004087154,0.01802352,0.4692794],"study_design_scores_gemma":[0.003791475,0.003043247,0.4367509,0.001808706,0.0003045977,0.004093,0.003071586,0.01371804,0.01269335,0.002927544,0.5165429,0.001254635],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9739661,0.002542675,0.0006622784,0.0009407951,0.0004912238,0.0001738921,0.00005833164,0.00001124035,0.02115349],"genre_scores_gemma":[0.9876702,0.002596133,0.002682142,0.0002910811,0.0007650429,1.55596e-8,0.00003237844,0.00001553337,0.005947447],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4985194,"threshold_uncertainty_score":0.998781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03424231724055606,"score_gpt":0.2951008958478597,"score_spread":0.2608585786073037,"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."}}