{"id":"W3002017077","doi":"10.1364/oe.381609","title":"Multispectral imaging via nanostructured random broadband filtering","year":2020,"lang":"en","type":"article","venue":"Optics Express","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Multispectral image; Spectral imaging; Broadband; Computer science; Optics; Image resolution; Full spectral imaging; Spectral bands; Spectral resolution; Remote sensing; Hyperspectral imaging; Filter (signal processing); Computer vision; Artificial intelligence; Telecommunications; Physics; Spectral line; Geology","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":[0.0002067573,0.0003077351,0.0003109551,0.0003225478,0.0001735063,0.0003608619,0.0003275827,0.0005687103,0.0006369165],"category_scores_gemma":[0.0003727148,0.0002189611,0.0002263422,0.0003101959,0.0002719336,0.0007502968,0.0005190438,0.0003325726,0.0003862363],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003344307,"about_ca_system_score_gemma":0.0001656157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003036651,"about_ca_topic_score_gemma":0.0006902464,"domain_scores_codex":[0.9997273,0.00003844504,0.00001096147,0.00007957517,0.000119725,0.00002397328],"domain_scores_gemma":[0.9997438,0.00006256569,0.0000815707,0.00004448278,0.00004885962,0.0000187479],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007397354,0.00003579529,0.0004071451,0.00004826058,0.00001313592,0.00008323791,0.00003820148,0.005224586,0.9592429,0.002579382,0.0003015343,0.03195189],"study_design_scores_gemma":[0.00002479664,0.0003056639,0.002082389,0.00002088531,0.00002503939,0.0006041161,0.00003489019,0.2554882,0.7328656,0.002641598,0.005842372,0.00006435791],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2900623,0.0009519644,0.7013756,0.0004047161,0.00009714556,0.00006091884,0.0001318045,0.0008299706,0.006085664],"genre_scores_gemma":[0.7104045,0.000481229,0.2863938,0.0002433739,0.00005365365,0.00006419719,0.00009352509,0.00003773987,0.002227899],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0006369165,"threshold_uncertainty_score":0.002426445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006747307591214158,"score_gpt":0.2158544004595706,"score_spread":0.2091070928683564,"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."}}