{"id":"W2626032759","doi":"10.5772/67351","title":"M-ary Optical Computing","year":2017,"lang":"en","type":"book-chapter","venue":"InTech eBooks","topic":"Optical Network Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; Program for New Century Excellent Talents in University; Wuhan National Laboratory for Optoelectronics; Wuhan Science and Technology Project; National Natural Science Foundation of China; National Program for Support of Top-notch Young Professionals; College of Family Physicians of Canada","keywords":"Computer science; Optical computing; Bottleneck; Cloud computing; Subtraction; Digital signal processing; Parallel computing; Signal processing; Computer hardware; Computational science; Electronic engineering; Embedded system; Arithmetic; Engineering; Mathematics; Operating system","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001258934,0.0007854372,0.0003490154,0.0008805639,0.0006133018,0.001576888,0.0006823168,0.0006497347,0.02573769],"category_scores_gemma":[0.0003265491,0.0003036333,0.00029521,0.001136658,0.0006671374,0.002219931,0.0009703019,0.001435216,0.01468011],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007596651,"about_ca_system_score_gemma":0.0004442647,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003062924,"about_ca_topic_score_gemma":0.0005007266,"domain_scores_codex":[0.9998432,0.00001081199,0.000004550648,0.00003240037,0.00008775172,0.00002125125],"domain_scores_gemma":[0.9999119,0.00002225728,0.000007167597,0.00001862857,0.00002790362,0.0000121626],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00007993147,0.00005221278,0.0001227247,0.001411296,0.000019636,0.0001807451,0.0003205648,0.001571221,0.02863258,0.2384835,0.1395487,0.5895768],"study_design_scores_gemma":[0.000004325339,0.00003287576,0.0001743437,0.0002642605,0.00000519064,0.0003840138,0.00003821034,0.001700421,0.006274171,0.03201031,0.9590995,0.0000124277],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.006658741,0.16013,0.04120995,0.001831681,0.005224315,0.00009509568,0.0002458978,0.0009085632,0.7836957],"genre_scores_gemma":[0.06702215,0.1535868,0.03976608,0.001527659,0.002399337,0.0001491731,0.0004691757,0.0003038815,0.7347758],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.02573769,"threshold_uncertainty_score":0.08610117,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02080984827034142,"score_gpt":0.2314852287879643,"score_spread":0.2106753805176229,"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."}}