{"id":"W4297798158","doi":"10.1364/cleo_at.2022.jw3b.43","title":"A Simple Way to Incorporate Loss When Modelling Multimode Entangled State Generation","year":2022,"lang":"en","type":"article","venue":"Conference on Lasers and Electro-Optics","topic":"Quantum optics and atomic interactions","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Multi-mode optical fiber; Lossy compression; Mixing (physics); Photon; Set (abstract data type); Simple (philosophy); Physics; Four-wave mixing; State (computer science); Spontaneous parametric down-conversion; Computer science; Quantum mechanics; Nonlinear optics; Optics; Quantum entanglement; Optical fiber; Algorithm; Laser; Quantum","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000113711,0.0001782201,0.0001792202,0.00008579899,0.0004596324,0.0001442017,0.0001203728,0.00001898214,0.0003524409],"category_scores_gemma":[0.000001965629,0.0001834363,0.00005121189,0.0001040296,0.00002293706,0.00009545728,0.00007764527,0.0002866504,0.00002226399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006085214,"about_ca_system_score_gemma":0.00008580833,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002294228,"about_ca_topic_score_gemma":0.00002150592,"domain_scores_codex":[0.9989347,0.00004683556,0.0002230284,0.0003064713,0.0001586693,0.0003302429],"domain_scores_gemma":[0.9994603,0.0000292334,0.0001013213,0.0001803724,0.00009409835,0.0001346886],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002602913,0.0005014085,0.001167899,0.00001408161,0.0001869182,0.00001743122,0.002721929,0.4289973,0.05989317,0.4894215,0.003006936,0.01381115],"study_design_scores_gemma":[0.0003846761,0.0002859604,0.000007232063,0.000006449147,0.00002045173,0.000001361048,0.0004334435,0.9493124,0.009478762,0.03815693,0.001672951,0.0002393765],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8661845,0.000005458369,0.1315342,0.0004735978,0.0001226042,0.0002033999,0.000143676,0.00002202734,0.001310462],"genre_scores_gemma":[0.9967309,0.00001255284,0.001769896,0.0002750035,0.0001202435,0.0000775728,0.0001963564,0.00002254364,0.000794988],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5203151,"threshold_uncertainty_score":0.7480315,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03016055522275922,"score_gpt":0.2557972495782099,"score_spread":0.2256366943554507,"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."}}