{"id":"W2958407396","doi":"10.1145/3306346.3322946","title":"Compact snapshot hyperspectral imaging with diffracted rotation","year":2019,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Optical Polarization and Ellipsometry","field":"Engineering","cited_by":179,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Ministry of Science and ICT, South Korea; Samsung; Institute for Information and Communications Technology Promotion; Electronics and Telecommunications Research Institute; National Research Foundation of Korea; SK Hynix","keywords":"Hyperspectral imaging; Optics; Spectral imaging; Snapshot (computer storage); Coded aperture; Computer science; Imaging spectrometer; Point spread function; Wavelength; Chromatic aberration; Physics; Artificial intelligence; Detector; Chromatic scale; Spectrometer","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004102283,0.0001430517,0.0001231199,0.0002680611,0.00007923607,0.00004811219,0.0001216858,0.00005774713,0.0003392077],"category_scores_gemma":[0.000006050474,0.0001305695,0.00006168669,0.0006666166,0.00003619252,0.0002283846,6.748355e-7,0.0003443303,0.0001221596],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003757258,"about_ca_system_score_gemma":0.00001005212,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000792834,"about_ca_topic_score_gemma":0.000009482344,"domain_scores_codex":[0.9993225,0.00001329139,0.0001292246,0.0001468499,0.0001796939,0.0002084125],"domain_scores_gemma":[0.9994588,0.00007995626,0.00001531011,0.0003166423,0.0000403698,0.00008890908],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004222718,0.001321027,0.09071757,0.0004595672,0.0009916243,0.00003093925,0.001998752,0.7834675,0.08342145,0.01515389,0.0009837921,0.0210316],"study_design_scores_gemma":[0.006366085,0.000834728,0.3006755,0.0003102029,0.0004074359,0.0001080897,0.002014924,0.6357716,0.03981757,0.005164743,0.005862648,0.002666476],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8261957,0.00005755452,0.1666914,0.0007516491,0.0004374629,0.0002819547,0.00002519233,0.0005848443,0.004974192],"genre_scores_gemma":[0.9980797,0.0000422327,0.001507333,0.0002231457,0.00001727219,0.000003603114,0.00003150823,0.00003497692,0.00006021925],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2099579,"threshold_uncertainty_score":0.532447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00816286932517825,"score_gpt":0.2120902456833104,"score_spread":0.2039273763581321,"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."}}