{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001302896,0.001706116,0.0008169929,0.003880006,0.002883775,0.002101123,0.001828585,0.001032978,0.03655757],"category_scores_gemma":[0.001238187,0.0005066084,0.0005173989,0.002829066,0.0006917459,0.001596503,0.001479822,0.001338666,0.01751178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00756337,"about_ca_system_score_gemma":0.01091401,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6852496,"about_ca_topic_score_gemma":0.8086244,"domain_scores_codex":[0.9977435,0.00008903485,0.0000267021,0.0002994228,0.001603555,0.0002378185],"domain_scores_gemma":[0.9978564,0.00003726804,0.0000482883,0.0001291823,0.001809401,0.0001193565],"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":[0.0003710956,0.00008129881,0.004360284,0.000235236,0.00009858655,0.0000936783,0.0001897864,0.002577244,0.04266289,0.01311102,0.6718411,0.2643777],"study_design_scores_gemma":[0.00008954389,0.00003498889,0.01780661,0.00007938727,0.00004919335,0.0001873919,0.0001403655,0.02149881,0.01845381,0.002229821,0.9392618,0.0001682314],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"methods","genre_scores_codex":[0.03320512,0.006309341,0.138662,0.003520133,0.001642997,0.001721755,0.1586301,0.05673133,0.5995772],"genre_scores_gemma":[0.1416972,0.004130217,0.361872,0.002591914,0.0004144146,0.001338936,0.183256,0.005734222,0.2989652],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.3147504,"threshold_uncertainty_score":0.633208,"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."}}