{"id":"W6894302893","doi":"10.5683/sp3/wxlsk3","title":"SRIX4VEG: Surface Reflectance Intercomparison Exercise for Vegetation - NRC, ARSL, NEO","year":2023,"lang":"en","type":"dataset","venue":"Borealis","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; National Research Council Canada","funders":"European Space Agency","keywords":"Metadata; Vegetation (pathology); Satellite; Satellite imagery; Earth observation satellite; Hyperspectral imaging; Earth observation; Reflectivity","routes":{"ca_aff":true,"ca_fund":false,"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":[0.01354066,0.001942822,0.001315972,0.001766573,0.00106744,0.002022525,0.004335386,0.001594234,0.01318932],"category_scores_gemma":[0.006201336,0.001080817,0.001804706,0.001892166,0.0005612853,0.003184803,0.002677057,0.001822863,0.01133293],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001287307,"about_ca_system_score_gemma":0.003075693,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01837755,"about_ca_topic_score_gemma":0.01844949,"domain_scores_codex":[0.997205,0.0008598079,0.0002429595,0.0004621517,0.001005464,0.0002246083],"domain_scores_gemma":[0.9959758,0.0005647638,0.0002625135,0.001426217,0.001480062,0.0002905879],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001474246,0.0008221537,0.01817726,0.001406828,0.0007573689,0.0004378175,0.001209873,0.06071552,0.04284114,0.008668466,0.7080204,0.1554688],"study_design_scores_gemma":[0.00214646,0.00052371,0.04779521,0.000617063,0.0002365847,0.0002466164,0.0009392838,0.1224929,0.03565087,0.01379364,0.775158,0.0003996371],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.07111221,0.0008813895,0.2280846,0.002251499,0.000833506,0.004181901,0.4325408,0.1830092,0.0771049],"genre_scores_gemma":[0.09215493,0.0003479843,0.3985044,0.0009615062,0.0001326323,0.005226258,0.4447179,0.04521848,0.01273586],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01837755,"threshold_uncertainty_score":0.07161069,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05056705114710072,"score_gpt":0.3464719086097275,"score_spread":0.2959048574626267,"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."}}