{"id":"W4320522336","doi":"10.1117/12.2650841","title":"An end-to-end adaptive neural network for process-aware snapshot compressive temporal imaging","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Optical Sensing Technologies","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec","funders":"","keywords":"Computer science; Snapshot (computer storage); Artificial intelligence; Compressed sensing; Convolutional neural network; Robustness (evolution); Computer vision; Data cube; Iterative reconstruction; Shearing (physics); Deep learning; Pattern recognition (psychology); Data mining; Geology","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":[0.0002745965,0.0006795077,0.0003330425,0.0001724059,0.0002480593,0.0003959304,0.001136417,0.0006243834,0.001736455],"category_scores_gemma":[0.0006011942,0.0002518105,0.0002186749,0.0001936678,0.0004031219,0.0006441572,0.0006892945,0.0009913457,0.0004641538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006220482,"about_ca_system_score_gemma":0.0009518249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005523902,"about_ca_topic_score_gemma":0.01218096,"domain_scores_codex":[0.9998547,0.00001257921,0.000005325073,0.00004930304,0.00005122189,0.00002679941],"domain_scores_gemma":[0.9998434,0.00003750796,0.00001975718,0.00001995744,0.00006148322,0.0000179018],"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":[0.0003414741,0.0002601693,0.002061883,0.0001205368,0.00007347536,0.0002159548,0.000087657,0.5623226,0.06748713,0.007623526,0.007269776,0.3521358],"study_design_scores_gemma":[0.000003934227,0.00003235119,0.0001100892,0.000002638133,0.000004832329,0.0000140218,0.000003161481,0.9943673,0.004283381,0.0005960729,0.0005774676,0.000004732504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04263289,0.0002958406,0.9495023,0.0003123963,0.000106582,0.00007573254,0.0001377436,0.002153619,0.004782925],"genre_scores_gemma":[0.7207181,0.000245795,0.2667569,0.0003724599,0.00005085559,0.0001606966,0.0004835401,0.00008361151,0.01112801],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005523902,"threshold_uncertainty_score":0.01098347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02569190817558576,"score_gpt":0.313188408052244,"score_spread":0.2874964998766582,"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."}}