{"id":"W2083022102","doi":"10.1080/07038992.2014.945517","title":"Iceberg Detection Using Simulated Dual-Polarized Radarsat Constellation Data","year":2014,"lang":"en","type":"article","venue":"Canadian Journal of Remote Sensing","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Defence Research and Development Canada","keywords":"Constellation; Iceberg; Synthetic aperture radar; Polarimetry; Remote sensing; Geography; Pixel; Computer science; Physics; Artificial intelligence; Optics; Meteorology; Sea ice; Astronomy; Scattering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.0007175475,0.0001082705,0.0001910351,0.000203152,0.0003327848,0.00009259785,0.000131142,0.00008511792,0.0001018766],"category_scores_gemma":[0.0002715699,0.000100497,0.00004526336,0.0001949538,0.00009625675,0.0003469637,0.000006031411,0.0002551706,0.00001042637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003516104,"about_ca_system_score_gemma":0.0004962215,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.08396104,"about_ca_topic_score_gemma":0.07420692,"domain_scores_codex":[0.9989438,0.0001382768,0.0003259483,0.0001404148,0.0001728589,0.0002787024],"domain_scores_gemma":[0.9988163,0.0001634127,0.0002783428,0.0002242312,0.000131469,0.0003862383],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003469109,7.355111e-7,0.009240094,0.00001155797,0.00004469994,0.0001442323,0.0002512314,0.03590328,0.0004377427,0.000001730218,0.00001708472,0.9539129],"study_design_scores_gemma":[0.0003121921,0.00004152332,0.002332261,0.00007633777,0.00006382698,0.001415386,0.0001826493,0.9854584,0.00005879675,0.0002765532,0.00965879,0.000123339],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8499731,0.00007918268,0.1478505,0.0001704928,0.0009910396,0.00005315938,0.00001590831,0.000007410739,0.0008591946],"genre_scores_gemma":[0.9589563,0.00000887137,0.04046433,0.0001967155,0.0003208916,5.873687e-12,0.00003437407,0.000005727029,0.00001276613],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9537896,"threshold_uncertainty_score":0.9426864,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02627899124382099,"score_gpt":0.2227170683507559,"score_spread":0.1964380771069349,"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."}}