{"id":"W2894897083","doi":"10.1007/978-3-030-01219-9_4","title":"Coded Two-Bucket Cameras for Computer Vision","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; 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":[0.0002129306,0.0007222267,0.0003856007,0.0008656881,0.000235586,0.001175335,0.001266705,0.0009226144,0.03115991],"category_scores_gemma":[0.0005586612,0.0004269819,0.0002325952,0.001430402,0.0004888361,0.001675058,0.001126028,0.001425724,0.008521655],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006508026,"about_ca_system_score_gemma":0.0004999366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007247271,"about_ca_topic_score_gemma":0.001639836,"domain_scores_codex":[0.9997384,0.00002768505,0.00000812464,0.00005054545,0.0001504509,0.00002482276],"domain_scores_gemma":[0.9998052,0.00005580335,0.00001092363,0.00004528039,0.00006983904,0.00001301371],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000143404,0.00004453201,0.00007358104,0.0006287208,0.00002470384,0.00005607956,0.00007621352,0.004542014,0.07991195,0.08937664,0.04927836,0.7758438],"study_design_scores_gemma":[0.00007910547,0.00015646,0.0006548368,0.0004320635,0.00004043442,0.001212011,0.0001052721,0.1539933,0.1581879,0.08109211,0.6039369,0.0001095939],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002732046,0.008486328,0.9423775,0.0003426028,0.0004569313,0.00008761709,0.0002996083,0.001390788,0.04382667],"genre_scores_gemma":[0.07628772,0.01240806,0.7900329,0.0005011809,0.0003202558,0.0002096642,0.001096267,0.0005946173,0.1185494],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03115991,"threshold_uncertainty_score":0.1042403,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01358463717225939,"score_gpt":0.2742658791457199,"score_spread":0.2606812419734605,"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."}}