{"id":"W2088176914","doi":"10.1109/ccece.2006.277344","title":"AIRIS the Canadian Hyperspectral Imager","year":2006,"lang":"en","type":"article","venue":"","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Hyperspectral imaging; Remote sensing; Imaging spectrometer; Flight test; Pixel; Spectrometer; Computer science; Detector; Electromagnetic spectrum; Data processing; Environmental science; Telecommunications; Geography; Artificial intelligence; Optics; Database; Physics; Simulation","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":[],"consensus_categories":[],"category_scores_codex":[0.00004979219,0.00006055376,0.0000390634,0.00004215089,0.00008028831,0.00007163735,0.00007191408,0.00003232731,0.00008128358],"category_scores_gemma":[0.000008271569,0.00004417591,0.00002346249,0.00009961935,0.000033255,0.00005478775,0.000002518123,0.00008298535,0.0003378867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001789356,"about_ca_system_score_gemma":0.00002744114,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.04678778,"about_ca_topic_score_gemma":0.1405718,"domain_scores_codex":[0.9996121,0.000006714314,0.00007063254,0.00006801123,0.0000651105,0.0001774313],"domain_scores_gemma":[0.9997251,0.00001343826,0.000005055744,0.0001927588,0.000022961,0.00004068156],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000105616,0.000009278718,0.0007426963,0.00001049434,0.00002204339,0.00002858107,0.0001551418,0.01805311,0.2784297,0.007989769,0.684493,0.01006506],"study_design_scores_gemma":[0.0002967783,0.00001078525,0.18916,0.00001052379,0.00003061924,0.0000806946,0.0001834698,0.4110088,0.1846735,0.002605454,0.2113453,0.0005940894],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1416367,0.0001318821,0.004292405,0.006465095,0.000330958,0.0001368571,0.000002709304,0.0004946334,0.8465087],"genre_scores_gemma":[0.9862854,0.000002105992,0.00856998,0.000138394,0.000140248,9.642088e-7,0.000005102142,0.00001936619,0.004838375],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8446487,"threshold_uncertainty_score":0.9595597,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006340271494133426,"score_gpt":0.1743430552370868,"score_spread":0.1680027837429533,"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."}}