{"id":"W6950380638","doi":"10.5683/sp3/rmgoiw","title":"Mer Bleue QA4EO Airborne Hyperspectral Imagery","year":2022,"lang":"en","type":"dataset","venue":"Borealis","topic":"","field":"","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; National Research Council Canada","funders":"","keywords":"Pixel; Hyperspectral imaging; Radiance; Satellite imagery; Atmospheric correction; Satellite; Thematic Mapper; Image processing","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":[0.0005106204,0.0004624003,0.0003699255,0.001195457,0.0003743187,0.0007170646,0.0007420923,0.0004114211,0.02718604],"category_scores_gemma":[0.0007260532,0.0002469603,0.0003052011,0.001262341,0.00012657,0.0008252269,0.0005041341,0.0005398273,0.01583371],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003677934,"about_ca_system_score_gemma":0.0004335527,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01817729,"about_ca_topic_score_gemma":0.03271497,"domain_scores_codex":[0.9995381,0.00005675925,0.00001050739,0.0001019625,0.0002483944,0.0000442879],"domain_scores_gemma":[0.9996773,0.00002039021,0.00001739424,0.00006040412,0.0001953933,0.00002904968],"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":[0.0006311524,0.0001732357,0.0142057,0.0002971319,0.0001227088,0.0001723376,0.0001941723,0.009714467,0.02381708,0.001722862,0.8463456,0.1026035],"study_design_scores_gemma":[0.0003000614,0.0001045704,0.07073384,0.0001534345,0.00003759443,0.0001699564,0.0002943306,0.04147175,0.01127952,0.001802624,0.8735301,0.0001222414],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.09179734,0.001071873,0.03454005,0.0009949936,0.0004443858,0.000794597,0.7025261,0.0163105,0.1515203],"genre_scores_gemma":[0.1549243,0.0003722297,0.05828986,0.000602404,0.0001157036,0.000454281,0.7486323,0.002630594,0.03397829],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02718604,"threshold_uncertainty_score":0.09094638,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01841899150621042,"score_gpt":0.2677614381876633,"score_spread":0.2493424466814529,"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."}}