{"id":"W4412399322","doi":"10.1364/oe.567917","title":"Toward in-sensor imaging classification enabled by on-chip all-optical modulation and photonic neural networks","year":2025,"lang":"en","type":"article","venue":"Optics Express","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; University of British Columbia","funders":"British Columbia Knowledge Development Fund; Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Optics; Modulation (music); Photonics; Artificial neural network; Chip; Image sensor; Materials science; Optoelectronics; Computer science; Telecommunications; Physics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003576511,0.0004150118,0.0003228468,0.0002877882,0.0001958531,0.0006256418,0.0007979905,0.0005525089,0.001109447],"category_scores_gemma":[0.0006713303,0.0001911763,0.0001800653,0.000309469,0.0003755827,0.001052166,0.0005004563,0.0006313575,0.0003144421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004168671,"about_ca_system_score_gemma":0.0003158019,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006704042,"about_ca_topic_score_gemma":0.001191652,"domain_scores_codex":[0.9997805,0.00005691266,0.00001192899,0.00004255619,0.00007247984,0.00003571417],"domain_scores_gemma":[0.999756,0.00007040134,0.00005971324,0.00004008999,0.00005731388,0.00001637894],"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":[0.0004458131,0.0003760055,0.002830432,0.0003120964,0.00008696645,0.0002034353,0.0001740178,0.03369173,0.6526651,0.01480723,0.004300365,0.2901068],"study_design_scores_gemma":[0.00001778052,0.0001419728,0.0009957842,0.00001570419,0.00002892572,0.0001297844,0.0000441889,0.6657281,0.3211477,0.004962398,0.006762716,0.00002490787],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3280187,0.002125603,0.6577712,0.001221772,0.0002605326,0.00008980809,0.0001960149,0.002283151,0.008033285],"genre_scores_gemma":[0.8423654,0.000627084,0.1534137,0.0002468976,0.00006381268,0.000044272,0.0001522336,0.00006058436,0.003025983],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001109447,"threshold_uncertainty_score":0.003711462,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02208402181704573,"score_gpt":0.2563780056023018,"score_spread":0.2342939837852561,"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."}}