{"id":"W2738956978","doi":"10.1525/elementa.154","title":"A practical algorithm for the retrieval of floe size distribution of Arctic sea ice from high-resolution satellite Synthetic Aperture Radar imagery","year":2017,"lang":"en","type":"article","venue":"Elementa Science of the Anthropocene","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Environment Research Council; Sight Research UK","keywords":"Ground truth; Synthetic aperture radar; Algorithm; Remote sensing; Sea ice; Computer science; Satellite; Artificial intelligence; Geology; Computer vision; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.001358858,0.0001429412,0.0002518885,0.00001927009,0.001225677,0.00006406584,0.00104253,0.00005398402,0.0002949288],"category_scores_gemma":[0.002091439,0.00008176234,0.0001347852,0.0002357977,0.004099278,0.0005152914,0.0001290657,0.0001521595,0.000003872661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002950167,"about_ca_system_score_gemma":0.0002744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00526186,"about_ca_topic_score_gemma":0.0001015862,"domain_scores_codex":[0.9980511,0.00008014786,0.0004048795,0.0003098568,0.0007971185,0.0003569065],"domain_scores_gemma":[0.9969255,0.001300386,0.0007060816,0.0007626163,0.0002356097,0.00006984556],"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":[0.002321691,0.001169382,0.4706087,0.0005789166,0.0006483687,0.00001490238,0.0013357,0.0008188201,0.03472836,0.004196788,0.001460418,0.482118],"study_design_scores_gemma":[0.0006942876,0.0003249604,0.833813,0.0001698457,0.0002452965,0.00001471731,0.0009377425,0.1369776,0.02405384,0.002237436,0.0003328165,0.0001984639],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.931999,0.0004092406,0.04621234,0.01466803,0.00183504,0.0009697049,0.003699239,0.000014878,0.0001925335],"genre_scores_gemma":[0.9846801,0.0004327924,0.01463315,0.00007423724,0.00008688204,8.518563e-7,0.00006716442,0.000003797333,0.00002108823],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4819195,"threshold_uncertainty_score":0.998611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01515469104528073,"score_gpt":0.2707414202333054,"score_spread":0.2555867291880247,"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."}}