{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004628933,0.0002849147,0.000393214,0.0003857386,0.0001914594,0.00008748047,0.0001203265,0.000167438,0.000001725715],"category_scores_gemma":[0.0001036072,0.0003367212,0.0002206235,0.0007716891,0.0001027529,0.0001369067,0.00004483581,0.0002670386,0.00003298715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000272483,"about_ca_system_score_gemma":0.00008103834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003313547,"about_ca_topic_score_gemma":0.000004831372,"domain_scores_codex":[0.9980242,0.00005660252,0.0005934767,0.0004357766,0.0003352739,0.0005546348],"domain_scores_gemma":[0.9985682,0.0002045577,0.0001644381,0.0006769195,0.0002756024,0.0001102287],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005264414,0.000003269004,6.161355e-7,0.0001416678,0.00004432142,0.00001447464,0.0000868335,0.0188783,0.5872254,0.00002268228,0.0004159533,0.3931612],"study_design_scores_gemma":[0.0003249976,0.00001667521,0.0003822274,0.0001181003,0.00007583802,0.00009641837,0.0001736009,0.9506029,0.04513543,0.001213681,0.001557571,0.0003025872],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1015603,0.00005344912,0.8933904,0.0003963408,0.0005137257,0.0007234919,0.00001207393,0.001045228,0.002305039],"genre_scores_gemma":[0.4789422,0.00006420506,0.5202395,0.00002227181,0.0003674174,2.814099e-8,0.0000745033,0.0001184614,0.0001714461],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9317246,"threshold_uncertainty_score":0.9999085,"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."}}