{"id":"W2019577288","doi":"10.1115/1.4004203","title":"Metal Embedded Optical Fiber Sensors: Laser-Based Layered Manufacturing Procedures","year":2011,"lang":"en","type":"article","venue":"Journal of Manufacturing Science and Engineering","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Materials science; Laser; Fiber Bragg grating; Microscale chemistry; Electroplating; Machining; Optical fiber; Tungsten carbide; Aerospace; Fiber laser; Laser ablation; Thin film; Optoelectronics; Optics; Composite material; Fiber; Layer (electronics); Nanotechnology; Metallurgy","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":[0.0002577298,0.0004912056,0.000236001,0.0004493529,0.0001647687,0.0004995193,0.0008692146,0.000569149,0.0006275913],"category_scores_gemma":[0.0003425873,0.0003430739,0.0002709724,0.0002716123,0.0003052823,0.0007503919,0.0005503537,0.000537698,0.0005707771],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000375464,"about_ca_system_score_gemma":0.0002107932,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00030639,"about_ca_topic_score_gemma":0.0004939479,"domain_scores_codex":[0.9995473,0.00004286332,0.00002907062,0.0001050363,0.0002339637,0.00004164507],"domain_scores_gemma":[0.9997869,0.0000386328,0.00008161359,0.00004229433,0.00003832101,0.00001211792],"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":[0.00002021475,0.00002224961,0.0001552338,0.0001088353,0.000007271814,0.00005362291,0.00003197113,0.0003831352,0.98326,0.0011176,0.0001186359,0.01472134],"study_design_scores_gemma":[0.00000505923,0.0001725639,0.0004111685,0.00001265719,0.000008954055,0.0003259542,0.00001282834,0.004441569,0.9886962,0.0001731922,0.005722554,0.00001723676],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4229168,0.01288356,0.5438617,0.0004275311,0.0003277744,0.0004967883,0.0005316511,0.003149598,0.01540462],"genre_scores_gemma":[0.6026489,0.003187774,0.388147,0.0002198849,0.0000588757,0.0002206765,0.000200937,0.00006991088,0.005245934],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0008692146,"threshold_uncertainty_score":0.002724171,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0143178184134967,"score_gpt":0.2136235815604559,"score_spread":0.1993057631469592,"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."}}