{"id":"W4280617287","doi":"10.1038/s41467-022-30439-9","title":"Ultra-compact snapshot spectral light-field imaging","year":2022,"lang":"en","type":"article","venue":"Nature Communications","topic":"Metamaterials and Metasurfaces Applications","field":"Materials Science","cited_by":185,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Education and Child Care","funders":"Fundamental Research Funds for the Central Universities; University Grants Committee; City University of Hong Kong; Science, Technology and Innovation Commission of Shenzhen Municipality; National Natural Science Foundation of China; Guangdong Science and Technology Department","keywords":"Spectral imaging; Snapshot (computer storage); Optics; Imaging science; Image resolution; Monochrome; Phase imaging; Superresolution; Medical imaging; Light field; Planar; Optical imaging; Imaging spectroscopy; Computer science; Physics; Hyperspectral imaging; Computer vision; Artificial intelligence; Microscopy; Computer graphics (images)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001843627,0.0003517644,0.000265829,0.0002953812,0.00009520233,0.0003342018,0.0004252373,0.0003733138,0.001247676],"category_scores_gemma":[0.0003139614,0.0001777531,0.0001198686,0.0001981349,0.0002738982,0.0008146095,0.0006089479,0.0003929053,0.0004267454],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002142492,"about_ca_system_score_gemma":0.0001770379,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001590018,"about_ca_topic_score_gemma":0.0004260324,"domain_scores_codex":[0.9999084,0.00001169521,0.000004309652,0.00001981946,0.00004099023,0.00001470769],"domain_scores_gemma":[0.9997873,0.00006002691,0.00004774161,0.00003960305,0.00004049248,0.00002482854],"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":[0.00006421661,0.00001366811,0.0002993831,0.00008080641,0.000003934022,0.00009211834,0.00003556997,0.0006736441,0.9786442,0.002263966,0.0005078317,0.01732067],"study_design_scores_gemma":[0.000009167673,0.0001493912,0.001257754,0.0000160626,0.000008236728,0.000710412,0.00005149187,0.02626583,0.9647034,0.001306666,0.005494135,0.00002736977],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4356119,0.002788142,0.5468082,0.0005666874,0.0001602105,0.00007846959,0.0007573885,0.002136934,0.01109214],"genre_scores_gemma":[0.7003641,0.0009088067,0.2948352,0.0001589131,0.00006050743,0.00006042722,0.0004044107,0.00006591865,0.003141656],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001247676,"threshold_uncertainty_score":0.004173875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02162282166118363,"score_gpt":0.3066151304612724,"score_spread":0.2849923088000887,"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."}}