{"id":"W4380628213","doi":"10.1364/oe.489493","title":"Addressing the programming challenges of practical interferometric mesh based optical processors","year":2023,"lang":"en","type":"article","venue":"Optics Express","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Interferometry; Computer science; Astronomical interferometer; Scalability; Mesh networking; Diamond; Efficient energy use; Computation; Optical mesh network; Parallel computing; Optics; Diagonal; Mach–Zehnder interferometer; Wireless mesh network; Materials science; Algorithm; Telecommunications; Physics; Engineering; Electrical engineering","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":[0.0001073012,0.000152226,0.0001268761,0.0001324881,0.0002088901,0.0003903943,0.0005531652,0.0002260166,0.002394894],"category_scores_gemma":[0.0003999963,0.0001032832,0.0001010008,0.0001987331,0.0002842075,0.0008704022,0.0003899767,0.0003627208,0.0002921573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002776215,"about_ca_system_score_gemma":0.000317888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003306573,"about_ca_topic_score_gemma":0.000967015,"domain_scores_codex":[0.9999372,0.00001034839,0.000003171523,0.00001101009,0.0000239034,0.00001428775],"domain_scores_gemma":[0.9998565,0.00005780966,0.0000180608,0.00003134543,0.0000287074,0.000007470499],"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":[0.0004359565,0.0001067807,0.002260944,0.000521892,0.00005216131,0.0004175287,0.0002976718,0.2747237,0.3199304,0.1783143,0.004825233,0.2181135],"study_design_scores_gemma":[0.00002492744,0.0002562101,0.0005885662,0.00002618369,0.00001632968,0.0002338234,0.0001347035,0.8619925,0.08137886,0.0375146,0.01781663,0.00001652237],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2999741,0.0005173982,0.6795869,0.0007071226,0.0001098327,0.00003985776,0.0000879647,0.0007903644,0.01818646],"genre_scores_gemma":[0.8293349,0.0003097167,0.1662327,0.00008683085,0.00002449438,0.00006375574,0.00006330521,0.00005799216,0.003826332],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002394894,"threshold_uncertainty_score":0.008011699,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1502385530725699,"score_gpt":0.3486919753265334,"score_spread":0.1984534222539635,"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."}}