{"id":"W2979560605","doi":"10.3390/mi10100683","title":"Evolutionary Computation for Parameter Extraction of Organic Thin-Film Transistors Using Newly Synthesized Liquid Crystalline Nickel Phthalocyanine","year":2019,"lang":"en","type":"article","venue":"Micromachines","topic":"Organic Electronics and Photovoltaics","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"European Regional Development Fund; National Physical Laboratory; Natural Sciences and Engineering Research Council of Canada; Queen Mary University of London","keywords":"Materials science; Thin-film transistor; Transistor; Organic electronics; Phthalocyanine; Optoelectronics; Threshold voltage; Characterization (materials science); Fabrication; Differential scanning calorimetry; Annealing (glass); Mesophase; Nanotechnology; Liquid crystal; Voltage; Electrical engineering; Metallurgy; Thermodynamics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003597898,0.000616106,0.0005339775,0.0005036177,0.0002277039,0.0005148381,0.0004614411,0.0006648299,0.001635026],"category_scores_gemma":[0.001512832,0.0003267879,0.0005197295,0.0003911252,0.0002732458,0.0003210433,0.0003374841,0.0005291851,0.0001696553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004615469,"about_ca_system_score_gemma":0.0005206959,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002528738,"about_ca_topic_score_gemma":0.003544243,"domain_scores_codex":[0.9999186,0.00002376605,0.00000467528,0.00001748809,0.00002148743,0.00001400258],"domain_scores_gemma":[0.9995654,0.0003378327,0.00002485565,0.00001428548,0.00004648104,0.00001104992],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002916414,0.00002409951,0.0006802535,0.00006099797,0.00003684545,0.00006948849,0.0000333411,0.9601877,0.002642146,0.002308947,0.0002457262,0.03368118],"study_design_scores_gemma":[0.000003636429,0.00001068967,0.00007635792,0.000002839217,0.000004630559,0.000005070457,0.000004070223,0.9989773,0.0003250309,0.0004623448,0.0001266435,0.000001320269],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1595979,0.0006911752,0.8331069,0.0002270821,0.00005957569,0.00009391102,0.0001569579,0.0005900354,0.005476507],"genre_scores_gemma":[0.6988734,0.0003179204,0.2971684,0.0000790792,0.00002468415,0.0003062335,0.0003268641,0.00009367114,0.002809754],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002528738,"threshold_uncertainty_score":0.00546968,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009385264931593943,"score_gpt":0.230886020450744,"score_spread":0.2215007555191501,"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."}}