{"id":"W2001916848","doi":"10.1364/ao.45.000122","title":"One-dimensional to two-dimensional channel formatting with micro-optics for wavelength division multiplexing networks","year":2006,"lang":"en","type":"article","venue":"Applied Optics","topic":"Photonic and Optical Devices","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Optics; Multiplexing; Interfacing; Wavelength-division multiplexing; Insertion loss; Wavelength; Disk formatting; Diffraction efficiency; Channel (broadcasting); Arrayed waveguide grating; Diffraction grating; Grating; Physics; Channel spacing; Optoelectronics; Materials science; Computer science; Telecommunications; Computer hardware","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":[0.0002303134,0.0003401534,0.0003532818,0.0001077526,0.0002380295,0.00008373783,0.0002006106,0.0001545497,0.000005497094],"category_scores_gemma":[0.00001295027,0.0003220133,0.0000760631,0.0002428149,0.00004891831,0.0001142978,0.0001222642,0.0002825465,0.00003435162],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008727348,"about_ca_system_score_gemma":0.00002414802,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009661069,"about_ca_topic_score_gemma":0.00002048818,"domain_scores_codex":[0.9980784,0.000005783267,0.00043491,0.0003436814,0.0003595167,0.0007777214],"domain_scores_gemma":[0.9990196,0.0003320357,0.00006442388,0.0002602578,0.0001188344,0.0002048546],"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":[0.00006559972,0.0000630174,0.00001256266,0.00006511425,0.00005070233,0.000002747994,0.00006267206,0.9758176,0.009132345,0.0129095,0.0001454236,0.001672681],"study_design_scores_gemma":[0.001280687,0.00007800525,0.0002526478,0.0001037077,0.000042092,0.000006017898,0.00004334907,0.9865479,0.009527751,0.00130824,0.0003132694,0.0004963657],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6359798,0.0001790162,0.3531437,0.000132416,0.0004400778,0.001453659,0.00008378815,0.0006243891,0.007963196],"genre_scores_gemma":[0.7880139,0.000003179527,0.2110544,0.000291198,0.0002787446,0.00008693372,0.0001514708,0.00008526341,0.00003489181],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1520341,"threshold_uncertainty_score":0.9999232,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009795116110362389,"score_gpt":0.2082317891892142,"score_spread":0.1984366730788518,"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."}}