{"id":"W4416368564","doi":"10.1109/ojcoms.2025.3635533","title":"Automated, Interpretable and Efficient ML Models for Real-World Lightpaths’ Quality of Transmission Estimation","year":2025,"lang":"en","type":"article","venue":"IEEE Open Journal of the Communications Society","topic":"Advanced Optical Network Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; European Commission","keywords":"Inference; Adaptability; Artificial neural network; Process (computing); Feature selection; Convolutional neural network; Feature (linguistics); Dimensionality reduction; Domain (mathematical analysis); Software deployment","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006633647,0.00007959626,0.0002310769,0.00003430533,0.0001774116,0.00003761465,0.001379127,0.00006132696,8.051165e-7],"category_scores_gemma":[0.00005351424,0.0000577759,0.0001356677,0.0003020677,0.0001509498,0.0001866035,0.0002658305,0.0002418345,9.488838e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000103966,"about_ca_system_score_gemma":0.00003916816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001098906,"about_ca_topic_score_gemma":0.000009290216,"domain_scores_codex":[0.9992122,0.0000594615,0.0004928265,0.00005730703,0.00008343271,0.00009474476],"domain_scores_gemma":[0.998516,0.0004398725,0.0001842558,0.0006919526,0.0001458512,0.00002211002],"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":[0.00001717977,0.00005281721,0.0000225459,0.00009401888,0.00008397498,1.426509e-8,0.0005136922,0.9710084,0.003335289,0.01192726,0.001809618,0.01113524],"study_design_scores_gemma":[0.0003150507,0.00001372869,0.0001547091,0.0003904706,0.00004095619,9.006349e-7,0.0001904222,0.978419,0.00246295,0.01747969,0.0004768665,0.00005523743],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02803778,0.001712791,0.9647376,0.00237266,0.0001465034,0.0005216464,0.00001165701,0.0001250834,0.00233429],"genre_scores_gemma":[0.6442006,0.001184394,0.3545196,0.0000187821,0.00000239777,0.00001250025,7.294151e-7,0.000006907622,0.00005412814],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6161628,"threshold_uncertainty_score":0.2562784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04479750766269199,"score_gpt":0.3605859266080536,"score_spread":0.3157884189453616,"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."}}