{"id":"W4302282570","doi":"10.32920/ryerson.14668449.v1","title":"FPCB magnetic micromirror for laser marking/engraving systems","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Advanced Measurement and Metrology Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"CMC Microsystems","keywords":"Engraving; Laser scanning; Microelectromechanical systems; Laser; Scanner; Materials science; Optics; Substrate (aquarium); Polyimide; Computer science; Optoelectronics; Nanotechnology; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000398059,0.0003313381,0.0004164201,0.0002135598,0.00007907276,0.00004422733,0.0003529346,0.000262331,0.0003539279],"category_scores_gemma":[0.00002954537,0.0003535139,0.000170686,0.00006759786,0.00002086018,0.00003490326,0.0002642002,0.000573897,0.000006497312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002338891,"about_ca_system_score_gemma":0.00002536369,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001447985,"about_ca_topic_score_gemma":0.00001014139,"domain_scores_codex":[0.9987286,0.0000330349,0.0003449348,0.0003695894,0.0001366348,0.0003872501],"domain_scores_gemma":[0.999288,0.00007525427,0.00006387319,0.0004575834,0.00005391982,0.00006132475],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005952544,0.0004537026,0.005051102,0.02505424,0.00221909,0.0001021213,0.0007752585,0.1379167,0.4347934,0.01089756,0.3041833,0.07795824],"study_design_scores_gemma":[0.000708979,0.0002262122,0.0001402485,0.0002151548,0.0002230169,0.00001071242,0.0001424517,0.01784,0.02048437,0.003055789,0.9557533,0.001199805],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1133251,0.0414601,0.6987535,0.0003078892,0.02285712,0.0118854,0.0004824413,0.01435737,0.09657109],"genre_scores_gemma":[0.7030576,0.001412468,0.2605556,0.0002086518,0.0007542346,0.009867743,0.0005600859,0.0005412356,0.02304241],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.65157,"threshold_uncertainty_score":0.9998917,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02608408873696275,"score_gpt":0.2554455726160458,"score_spread":0.229361483879083,"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."}}