{"id":"W4246422677","doi":"10.5194/tcd-5-3129-2011","title":"Estimating ice phenology on large northern lakes from AMSR-E: algorithm development and application to Great Bear Lake and Great Slave Lake, Canada","year":2011,"lang":"en","type":"preprint","venue":"","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Phenology; Climatology; Physical geography; Radiometer; Environmental science; Geography; Geology; Remote sensing; Ecology; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004930936,0.0005530722,0.0004064125,0.0009686872,0.0006187868,0.0006313551,0.0007359666,0.0002602222,0.0007269623],"category_scores_gemma":[0.001032882,0.0002438903,0.0003164064,0.001139925,0.0002606863,0.0002066777,0.0004066227,0.0002624094,0.0001869529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002380179,"about_ca_system_score_gemma":0.00580946,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8098799,"about_ca_topic_score_gemma":0.837902,"domain_scores_codex":[0.9998652,0.00001106369,0.000007520301,0.00003794632,0.00004691979,0.00003135091],"domain_scores_gemma":[0.9996001,0.0000751867,0.00003266545,0.00001774307,0.000236053,0.00003824569],"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":[0.0004421723,0.0001941237,0.2191318,0.000158765,0.000310018,0.0003078464,0.0004627686,0.2737273,0.03005324,0.0006512064,0.004119016,0.4704417],"study_design_scores_gemma":[0.00003803258,0.00001775226,0.06931107,0.000004982979,0.00003245309,0.00002507733,0.0001330721,0.9272541,0.002414028,0.00009791904,0.0006568696,0.00001469903],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9149058,0.0002875439,0.08062577,0.0001580833,0.00001551543,0.0001635393,0.0007758911,0.001632979,0.001434916],"genre_scores_gemma":[0.8452974,0.0001968969,0.1498695,0.00005130782,0.00001307098,0.000107023,0.00194575,0.00009597473,0.002422914],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1901201,"threshold_uncertainty_score":0.3824795,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01038974449023845,"score_gpt":0.1970223720341721,"score_spread":0.1866326275439336,"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."}}