{"id":"W6891717563","doi":"10.4224/40003456","title":"MBASSS Sentinel-2/Landsat 8 Data Product Validation Project – Final Report","year":2017,"lang":"en","type":"report","venue":"NPARC","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Data validation; Hyperspectral imaging; Satellite; Product (mathematics); Field (mathematics); Earth observation satellite; Model validation; Missing data; Data integration","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.009431187,0.0007871737,0.0004190944,0.001189827,0.0005405897,0.001526958,0.001146228,0.0006014305,0.008659302],"category_scores_gemma":[0.008179426,0.0003941106,0.0004042642,0.001141557,0.0003894032,0.001135441,0.0008961543,0.0007604898,0.01211148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0012193,"about_ca_system_score_gemma":0.005631365,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02819183,"about_ca_topic_score_gemma":0.0213287,"domain_scores_codex":[0.9953934,0.0007490166,0.0001764544,0.0002550265,0.003249907,0.0001762775],"domain_scores_gemma":[0.9919087,0.0005918013,0.000390772,0.0007331588,0.006165977,0.0002095473],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007232492,0.0008159294,0.02049479,0.0005027939,0.0001437167,0.0002918732,0.0002524658,0.02962467,0.01555429,0.0086311,0.7281572,0.1948079],"study_design_scores_gemma":[0.0002654044,0.0004420524,0.02900411,0.0005092799,0.00007375597,0.0002172175,0.0003362489,0.03504412,0.04658804,0.004332168,0.8830804,0.0001071343],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.1001201,0.001119234,0.2198916,0.003352541,0.001331656,0.006532889,0.4955327,0.008150351,0.1639689],"genre_scores_gemma":[0.0546599,0.0008838228,0.1293158,0.0004768357,0.00009343035,0.003660663,0.7643986,0.003401609,0.04310943],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.02819183,"threshold_uncertainty_score":0.05605549,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2825913315338359,"score_gpt":0.419851875297953,"score_spread":0.1372605437641171,"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."}}