{"id":"W3004023158","doi":"10.3390/rs12030382","title":"Assessing the Performance of Methods for Monitoring Ice Phenology of the World’s Largest High Arctic Lake Using High-Density Time Series Analysis of Sentinel-1 Data","year":2020,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Synthetic aperture radar; Remote sensing; Phenology; Environmental science; Arctic; Physical geography; Geology; Oceanography; Geography; Ecology","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":[0.0008814163,0.0001383826,0.000473714,0.00008490665,0.000272206,0.00002880015,0.0003757583,0.00005395463,0.00003139953],"category_scores_gemma":[0.0003276225,0.00009150714,0.0001178688,0.001015748,0.0002734784,0.0003000069,0.0001373055,0.0001806463,6.688984e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000636347,"about_ca_system_score_gemma":0.00007511758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002168757,"about_ca_topic_score_gemma":0.0004371945,"domain_scores_codex":[0.998594,0.0002887172,0.0004277742,0.0002627723,0.0001853025,0.0002413712],"domain_scores_gemma":[0.9979706,0.0007946622,0.0005145351,0.0005167505,0.0001628145,0.00004060268],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000221649,0.00002531734,0.6238815,0.0008636467,0.001935601,0.000004472025,0.001757879,0.1905118,0.0340874,0.00004650217,0.000008500223,0.1466558],"study_design_scores_gemma":[0.00009562458,0.00001726664,0.1563614,0.00009002844,0.0009458372,0.000008073609,0.0003343404,0.8391662,0.00281344,0.0000408079,0.00004248252,0.0000844881],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9743472,0.00005153308,0.02454566,0.0005940667,0.000230262,0.0001155019,0.00006361712,0.00001116734,0.00004100412],"genre_scores_gemma":[0.8422351,0.00002143126,0.1574828,0.00005740724,0.0001064782,1.831355e-9,0.00006981245,0.000005708709,0.0000212524],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6486545,"threshold_uncertainty_score":0.3731553,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04994262078771591,"score_gpt":0.3155481909459151,"score_spread":0.2656055701581992,"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."}}