{"id":"W2167552366","doi":"10.1109/icmens.2003.1221964","title":"Laser micromachining for microfluidic, microelectronic and MEMS applications","year":2004,"lang":"en","type":"article","venue":"","topic":"Laser Material Processing Techniques","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Materials science; Laser drilling; Surface micromachining; Fluence; Laser; Laser beam machining; Laser ablation; Microelectronics; Microelectromechanical systems; Optoelectronics; Optics; Substrate (aquarium); Fabrication; Drilling; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005984694,0.0001028175,0.00009329823,0.00004187112,0.0000690724,0.00005916431,0.00008862648,0.00005321625,0.00000876778],"category_scores_gemma":[0.000002286156,0.00009979769,0.00001972145,0.00005486609,0.0000249848,0.00008418117,0.0000189718,0.00005150776,0.000009306485],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005480586,"about_ca_system_score_gemma":0.00001635127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001583453,"about_ca_topic_score_gemma":0.00000602286,"domain_scores_codex":[0.9995308,0.000001817895,0.0001146936,0.00013283,0.0000269804,0.000192866],"domain_scores_gemma":[0.9998105,0.00001221104,0.000011398,0.0001126321,0.00001699268,0.00003633626],"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":[0.000002022575,0.000007940311,0.000005206666,0.00007222185,0.00001205404,2.303728e-7,0.00003659655,0.00001988396,0.9923219,0.0008857175,0.00167727,0.004958959],"study_design_scores_gemma":[0.000269409,0.00002110556,0.000007985943,0.00001331296,0.00001043757,0.00001368176,0.000006095565,0.000103497,0.958427,0.00792024,0.03307452,0.0001327117],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2734893,0.002846227,0.7195122,0.0002000648,0.00006509575,0.000630995,0.00002137353,0.00195184,0.001282848],"genre_scores_gemma":[0.9590885,0.0002401456,0.04004301,0.0001172418,0.00007787415,0.0002801487,0.00001991275,0.00004884672,0.00008433761],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6855991,"threshold_uncertainty_score":0.4069632,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005486888523617956,"score_gpt":0.2169974819148008,"score_spread":0.2115105933911829,"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."}}