{"id":"W3092790950","doi":"","title":"How consistent is cloudiness over Canada from satellite observations and modeling data","year":2004,"lang":"en","type":"article","venue":"AGU Spring Meeting Abstracts","topic":"Atmospheric aerosols and clouds","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Cloud cover; Climatology; Satellite; Environmental science; Meteorology; Remote sensing; Geography; Computer science; Cloud computing; Geology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001914226,0.000154941,0.0001400906,9.290915e-7,0.0002235368,0.0001153663,0.0002993871,0.00005369956,0.00002400896],"category_scores_gemma":[0.0001320165,0.0001510696,0.00001798017,0.00008604681,0.00005865009,0.0002616525,0.0003986605,0.000135949,0.00000625703],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002201667,"about_ca_system_score_gemma":0.00008529148,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.888741,"about_ca_topic_score_gemma":0.6205655,"domain_scores_codex":[0.9986306,0.00001006074,0.0002201299,0.0004751942,0.0003823915,0.0002815751],"domain_scores_gemma":[0.9991635,0.00005682799,0.0001114999,0.0005166313,0.00001296382,0.0001385473],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00000950816,0.00005525245,0.6239759,0.00002404663,0.00005385416,0.00006876513,0.0005040629,0.3570673,0.01355999,0.00006545869,0.000571277,0.00404461],"study_design_scores_gemma":[0.0004240826,0.000008015352,0.9543475,0.0001312858,0.00004500755,0.000003627061,0.0003732516,0.03298734,0.001082903,0.0003182036,0.009888598,0.0003902018],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963053,0.0002992544,0.0003541912,0.001469985,0.0002062274,0.00008631786,0.00003806568,0.00003493309,0.001205732],"genre_scores_gemma":[0.991241,0.00008618054,0.007879493,0.0005621999,0.0000696258,0.000002489823,0.00001724102,0.00001791366,0.0001238552],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3303716,"threshold_uncertainty_score":0.616044,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0474782514601975,"score_gpt":0.2243094964551673,"score_spread":0.1768312449949699,"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."}}