{"id":"W2810179225","doi":"10.1364/aio.2017.ath2a.1","title":"High-speed in situ metrology for laser-based advanced manufacturing","year":2017,"lang":"en","type":"article","venue":"","topic":"Laser Material Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Aerospace; Metrology; Automotive industry; Quality assurance; Process (computing); Focus (optics); Laser; Computer science; Welding; Manufacturing engineering; Laser beams; Mechanical engineering; Process control; Quality (philosophy); Engineering; Aerospace engineering; Optics; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008339874,0.0005370521,0.0005759927,0.0008041353,0.0003336813,0.0009505257,0.0009744373,0.0009713336,0.003884871],"category_scores_gemma":[0.001588476,0.0004029499,0.0001565875,0.0007546218,0.0006325229,0.001755537,0.001102068,0.0008156268,0.00099258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006075653,"about_ca_system_score_gemma":0.0003397175,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002687192,"about_ca_topic_score_gemma":0.0004925773,"domain_scores_codex":[0.9993094,0.0001554581,0.00002435432,0.000112725,0.0003599244,0.00003819725],"domain_scores_gemma":[0.9991247,0.0003500048,0.0001475518,0.0001882675,0.000151182,0.00003826178],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000347398,0.00009162354,0.001521239,0.0004340344,0.0000231745,0.0001038519,0.0001646202,0.005946448,0.8097975,0.02202279,0.001947219,0.1576],"study_design_scores_gemma":[0.00008789169,0.0007294209,0.003451562,0.0001535977,0.00003998635,0.000943313,0.0002089161,0.2182351,0.6842422,0.02415537,0.06762369,0.0001289748],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06844072,0.005864252,0.9114925,0.0005139342,0.0004086097,0.0001524366,0.0002979684,0.002026925,0.01080269],"genre_scores_gemma":[0.5118821,0.001856497,0.4805293,0.0001376637,0.0001649297,0.0001180132,0.0002279499,0.0001409291,0.004942619],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003884871,"threshold_uncertainty_score":0.0129962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01326983338084709,"score_gpt":0.2551615336095835,"score_spread":0.2418917002287365,"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."}}