{"id":"W2046305484","doi":"10.1364/ol.39.006217","title":"Automatic laser welding and milling with in situ inline coherent imaging","year":2014,"lang":"en","type":"article","venue":"Optics Letters","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Keyhole; Welding; Laser; Microsecond; Laser beam welding; Exploit; Materials science; Robustness (evolution); Laser ablation; Computer science; In situ; Frame rate; Optics; Mechanical engineering; Engineering; Artificial intelligence; Physics","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":[0.0001048837,0.0001255505,0.0001272672,0.0001118994,0.00004017517,0.00005509503,0.00008063032,0.00002428114,0.000006080294],"category_scores_gemma":[0.00001190065,0.0001184646,0.0000163352,0.0002158341,0.00005681838,0.00009120459,0.00001844656,0.0001666154,0.00000895954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002691903,"about_ca_system_score_gemma":0.000002595154,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005024785,"about_ca_topic_score_gemma":0.00002383312,"domain_scores_codex":[0.9993622,0.00001156692,0.0001571957,0.0001455854,0.00009897021,0.0002245172],"domain_scores_gemma":[0.9996392,0.00009619879,0.0000187578,0.0001681762,0.00001165347,0.0000660533],"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":[0.00001935582,0.0001855239,0.1084721,0.0009961637,0.0001916173,0.00006784828,0.002187937,0.4575792,0.3390273,0.006391467,0.0007566751,0.08412477],"study_design_scores_gemma":[0.0004648967,0.00002006717,0.008304608,0.0001468921,0.00002686374,0.000007789573,0.00007739072,0.9860393,0.004142237,0.0001432514,0.0003259715,0.0003006866],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9659791,0.00004687109,0.03142858,0.001005679,0.00003194312,0.0001391947,0.000001059198,0.0001721484,0.001195414],"genre_scores_gemma":[0.9603227,0.000009027768,0.03929766,0.0002730648,0.0000315893,0.00003413048,0.000003828542,0.00002610714,0.000001896717],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5284601,"threshold_uncertainty_score":0.4830848,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004377136009780004,"score_gpt":0.1897385865294492,"score_spread":0.1853614505196692,"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."}}