{"id":"W4400228312","doi":"10.1364/ol.524529","title":"Automating physical intuition in nonlinear fiber optics with unsupervised dominant balance search","year":2024,"lang":"en","type":"article","venue":"Optics Letters","topic":"Advanced Fiber Laser Technologies","field":"Physics and Astronomy","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Nautical Research Society","funders":"Centre National de la Recherche Scientifique; Academy of Finland; Agence Nationale de la Recherche","keywords":"Intuition; Nonlinear system; Physics; Nonlinear optics; Statistical physics; Optics; Computer science; Optical fiber; Quantum mechanics","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":[0.00007616187,0.0001859137,0.0001940345,0.0001056411,0.00005303444,0.0001014242,0.0001728096,0.00003739801,0.00002474893],"category_scores_gemma":[0.000004684914,0.0001565389,0.00005108073,0.0003698252,0.0001208702,0.0002267028,0.00008762453,0.0004566768,0.00008628914],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006096456,"about_ca_system_score_gemma":0.00003419637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001456436,"about_ca_topic_score_gemma":0.00000106776,"domain_scores_codex":[0.9989445,0.0000194382,0.0001704316,0.0003169892,0.0001915153,0.0003571601],"domain_scores_gemma":[0.9995365,0.000111977,0.00002830791,0.0002601091,0.00002608151,0.00003698702],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001982731,0.001237987,0.03267273,0.001088383,0.0006210802,0.0009273798,0.007336432,0.2333145,0.3755463,0.1377381,0.001023677,0.2082952],"study_design_scores_gemma":[0.001022774,0.0001417193,0.0009583827,0.0006845582,0.00004857457,0.000006403712,0.0007008717,0.9344084,0.05866704,0.002209986,0.0005010113,0.0006503131],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9633164,0.00001462845,0.03373549,0.001599876,0.00005540688,0.0001944831,0.00002797595,0.0001761866,0.0008794984],"genre_scores_gemma":[0.9259164,0.000002155394,0.07360061,0.00007495402,0.0002102355,0.00003771455,0.00004131722,0.00004055043,0.00007608653],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7010939,"threshold_uncertainty_score":0.6383471,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008029597758989346,"score_gpt":0.2534936655682715,"score_spread":0.2454640678092822,"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."}}