{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0007899624,0.0009140539,0.001000692,0.0007344234,0.0003754439,0.0001818695,0.001768463,0.0004359092,0.07659727],"category_scores_gemma":[0.0003561986,0.0009597616,0.0005264257,0.0007967597,0.0003110635,0.0002384587,0.0007170177,0.001658197,0.002246709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009730645,"about_ca_system_score_gemma":0.0005859479,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1600914,"about_ca_topic_score_gemma":0.01592569,"domain_scores_codex":[0.9949933,0.0004094579,0.0006757053,0.001265513,0.001503039,0.001153018],"domain_scores_gemma":[0.9957846,0.0001681169,0.0005077232,0.003028644,0.0001392855,0.0003716534],"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":[0.0001187402,0.0002895677,0.000002028428,0.00007988431,0.0003131613,0.001130428,0.00004924793,0.00001209677,0.00008761214,0.00009758427,0.9977173,0.0001023933],"study_design_scores_gemma":[0.000554455,0.00009860013,0.0002561529,0.00002689178,0.0005665229,0.0001533683,0.000129516,0.000002940157,0.00009563444,0.0001130265,0.9969421,0.001060789],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000007253057,0.0008647483,5.332176e-7,0.0003379131,0.0005240771,0.0005245581,0.992829,0.0004467872,0.004465083],"genre_scores_gemma":[0.000001225462,0.0003571041,0.0001494378,0.0006807086,0.001013329,0.0003389888,0.9964917,0.0003767762,0.0005907502],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1441658,"threshold_uncertainty_score":0.9992853,"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."}}