{"id":"W1895514615","doi":"10.1109/igarss.1999.773485","title":"Sea ice parameters retrieval using synergetic observations from SSM/I 85 GHz and AVHRR","year":2003,"lang":"en","type":"article","venue":"","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Sea ice; Sea ice concentration; Remote sensing; Radiative transfer; Environmental science; Sea ice thickness; Sea surface temperature; Radiative forcing; Breakup; Geology; Forcing (mathematics); Atmospheric sciences; Climatology; Arctic ice pack; Oceanography; Climate change; Physics; Optics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002423973,0.0003605558,0.0003616987,0.0006648242,0.0002275806,0.0005187538,0.0002221053,0.0002034355,0.0008787811],"category_scores_gemma":[0.0004750011,0.000163601,0.0002018363,0.0006328552,0.0001021138,0.0004281878,0.0003011847,0.0001048163,0.0003798644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003119022,"about_ca_system_score_gemma":0.0005388219,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01125989,"about_ca_topic_score_gemma":0.01835971,"domain_scores_codex":[0.9998549,0.00002081269,0.000009492267,0.00004695404,0.00003881821,0.00002891271],"domain_scores_gemma":[0.9998727,0.00001289803,0.00002673658,0.00002057251,0.00005538444,0.00001174843],"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":[0.0008119592,0.0004724568,0.2770634,0.0002720942,0.0003237195,0.0002579096,0.0003520835,0.09775616,0.2427258,0.001541878,0.004516018,0.3739065],"study_design_scores_gemma":[0.0001952417,0.0004200106,0.6763254,0.00004193376,0.0002453354,0.0001882602,0.0002883309,0.2616695,0.05205065,0.0009625804,0.007545951,0.0000668516],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9786171,0.00009420398,0.01469853,0.00002925317,0.00001526501,0.00005291642,0.002869077,0.000356864,0.003266768],"genre_scores_gemma":[0.9678981,0.00006849835,0.02648161,0.00001481454,0.00001404706,0.00004265069,0.004248385,0.00002244682,0.001209385],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01125989,"threshold_uncertainty_score":0.0223887,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03553966753386281,"score_gpt":0.2163780699246803,"score_spread":0.1808384023908175,"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."}}