{"id":"W4322743777","doi":"10.3390/rs15051378","title":"Potential Assessment of PRISMA Hyperspectral Imagery for Remote Sensing Applications","year":2023,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Dalhousie University; Mitacs; Innovation Saskatchewan; Agriculture and Agri-Food Canada; SRM Institute of Science and Technology; University of Alberta; University of Saskatchewan; Wilfrid Laurier University; Athabasca University","keywords":"Hyperspectral imaging; Remote sensing; Environmental science; Computer science; Sensor fusion; Identification (biology); Geology; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002004853,0.0007570657,0.0002653631,0.001644534,0.0003195053,0.001233774,0.0006594666,0.0006045452,0.001658953],"category_scores_gemma":[0.002674467,0.0002136428,0.0005547934,0.001731447,0.0004026443,0.001785788,0.000654163,0.0004680472,0.0005937231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005349077,"about_ca_system_score_gemma":0.000430802,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003186729,"about_ca_topic_score_gemma":0.005497048,"domain_scores_codex":[0.9990557,0.0001600605,0.00002745174,0.0001007463,0.0005662661,0.00008985683],"domain_scores_gemma":[0.9987847,0.0003483048,0.00009809068,0.000085107,0.0006366817,0.00004716538],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008171053,0.0004411556,0.08292496,0.002025195,0.0002919236,0.002202573,0.0008329805,0.2123856,0.1879872,0.01453083,0.01478973,0.4807706],"study_design_scores_gemma":[0.00002882971,0.000360638,0.09599608,0.0001605442,0.0001764783,0.0008262387,0.001504421,0.806774,0.06121754,0.005240881,0.02762345,0.00009099185],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7979683,0.002438494,0.1507855,0.001175528,0.0001163587,0.0003798161,0.003376104,0.001305604,0.04245428],"genre_scores_gemma":[0.9223491,0.0006665302,0.07308774,0.0001141356,0.00002139275,0.00006654691,0.002023083,0.0001779326,0.001493526],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003186729,"threshold_uncertainty_score":0.01060277,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01954352197645103,"score_gpt":0.2816643772331734,"score_spread":0.2621208552567224,"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."}}