{"id":"W2034172429","doi":"10.1016/j.ultramic.2012.01.014","title":"Optimization of Q-factor of AFM cantilevers using genetic algorithms","year":2012,"lang":"en","type":"article","venue":"Ultramicroscopy","topic":"Force Microscopy Techniques and Applications","field":"Physics and Astronomy","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"National Science and Technology Council","keywords":"Cantilever; Q factor; Resolution (logic); Finite element method; Work (physics); Optimal design; Genetic algorithm; Materials science; Stress (linguistics); Structural engineering; Acoustics; Computer science; Physics; Mechanical engineering; Optoelectronics; Engineering; Mathematics; Composite material; Mathematical optimization; Resonator","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":[0.00004110307,0.0001117898,0.0001733598,0.00005058093,0.00004899645,0.00000864621,0.0001208213,0.00004156099,0.0003916799],"category_scores_gemma":[0.000001580792,0.0001125784,0.00007550028,0.0001636746,0.00009331654,0.00009208666,0.00002185328,0.00005761049,0.000002879914],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001932731,"about_ca_system_score_gemma":0.00004015525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006579053,"about_ca_topic_score_gemma":2.660064e-7,"domain_scores_codex":[0.9993144,0.00001261171,0.0002657977,0.0001128884,0.00007835128,0.0002159993],"domain_scores_gemma":[0.9994539,0.00001696369,0.0001978253,0.0002112057,0.00006643506,0.00005366352],"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":[0.000004142957,0.0001453645,0.05091386,0.00002140266,0.00002543389,1.840139e-8,0.0002552519,0.003614997,0.9429836,0.0009949467,0.00006959355,0.0009713653],"study_design_scores_gemma":[0.0001501784,0.00002210366,0.001977116,0.00002806454,0.00002966098,6.158492e-7,0.000113032,0.004003693,0.9931784,0.00006137758,0.0003128907,0.0001229015],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5567015,0.00004199917,0.4424622,0.000003334024,0.00005709603,0.0001523635,0.0001796534,0.0000144017,0.0003873754],"genre_scores_gemma":[0.7458091,0.000004778675,0.2540385,0.000005825042,0.00005352344,0.000005966444,0.00002785855,0.00001581875,0.00003857668],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1891076,"threshold_uncertainty_score":0.4590814,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01467187545773268,"score_gpt":0.2923741695445198,"score_spread":0.2777022940867871,"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."}}