{"id":"W2058207181","doi":"10.1016/j.optlaseng.2014.09.017","title":"Introducing a new optimization tool for femtosecond laser-induced surface texturing on titanium, stainless steel, aluminum and copper","year":2014,"lang":"en","type":"article","venue":"Optics and Lasers in Engineering","topic":"Laser Material Processing Techniques","field":"Engineering","cited_by":134,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Femtosecond; Materials science; Fluence; Laser; Titanium; Copper; Microstructure; Aluminium; Composite material; Coupling (piping); Surface (topology); Thermal conductivity; Optics; Metallurgy","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":[0.0002428666,0.0002165676,0.0002287005,0.0001051963,0.00004638812,0.0001553557,0.00008151306,0.0001130857,0.000004050211],"category_scores_gemma":[0.00008518625,0.0002358828,0.00001781594,0.00009676756,0.00001110656,0.0001834385,0.00003819283,0.000143458,5.69589e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000539094,"about_ca_system_score_gemma":0.00001149074,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008988608,"about_ca_topic_score_gemma":0.000008901991,"domain_scores_codex":[0.99916,0.000008332499,0.0002194898,0.0002490834,0.0000812117,0.0002818652],"domain_scores_gemma":[0.9995998,0.0001005454,0.00002700958,0.0001676947,0.00002370809,0.00008120602],"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":[0.00001403813,0.000006662735,0.0000596389,0.0003994716,0.0000126314,0.000001397104,0.0001200684,0.9697315,0.02746968,0.000415382,0.0001058301,0.001663742],"study_design_scores_gemma":[0.0005370147,0.00008044601,0.00006955663,0.0002279988,0.00001199679,0.000003088747,0.00002857514,0.9279373,0.07038456,0.00005550768,0.0003633407,0.0003006396],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8816547,0.00005419307,0.1171682,0.00005798321,0.0002785515,0.0002783811,0.00001139511,0.0003247646,0.0001718155],"genre_scores_gemma":[0.9003436,0.00006487403,0.0993031,0.0000243767,0.0001326074,0.00001391208,0.00001237352,0.00007230909,0.00003283141],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04291488,"threshold_uncertainty_score":0.9619023,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006270565903193349,"score_gpt":0.1993377613199136,"score_spread":0.1930671954167202,"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."}}