{"id":"W1599238524","doi":"10.1007/3-540-45749-6_48","title":"Wide-Sense Nonblocking WDM Cross-Connects","year":2002,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Optical Network Technologies","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Wavelength-division multiplexing; Wavelength; Computer science; Enhanced Data Rates for GSM Evolution; Graph; Constant (computer programming); Topology (electrical circuits); Optics; Mathematics; Telecommunications; Physics; Theoretical computer science; Combinatorics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001842698,0.0005272933,0.000470069,0.0004492868,0.0001406966,0.0002207378,0.0009120317,0.0005211189,0.00004826227],"category_scores_gemma":[0.0002272377,0.0005140585,0.00009439225,0.0003829039,0.001019825,0.0002541366,0.0004768316,0.001210229,0.00009870925],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003015676,"about_ca_system_score_gemma":0.00002941964,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001325648,"about_ca_topic_score_gemma":0.00001488531,"domain_scores_codex":[0.997547,0.000004270723,0.0003943354,0.0008164053,0.0004731558,0.0007648398],"domain_scores_gemma":[0.9982048,0.0006506381,0.00007518331,0.0008740252,0.00009450597,0.0001008954],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001492781,0.000003817649,0.00004719896,0.00003575972,0.000007662934,0.000168201,0.00005113978,0.7660478,0.00009493925,0.002388659,0.0000192539,0.2311341],"study_design_scores_gemma":[0.0001850049,0.00006673753,0.00005501726,0.0004743033,0.000007916917,0.00007631299,7.698421e-8,0.8601985,0.002694474,0.132359,0.002983774,0.0008988997],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0009474317,0.001168443,0.9841169,0.0001085896,0.001519539,0.0002325478,0.000004203133,0.001193561,0.01070881],"genre_scores_gemma":[0.4761168,0.0003208074,0.521699,0.0005474313,0.0006436625,0.00001040674,0.000003383846,0.0001559263,0.0005025759],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4751693,"threshold_uncertainty_score":0.9997311,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01345923116714745,"score_gpt":0.2307116155205096,"score_spread":0.2172523843533622,"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."}}