{"id":"W2066060248","doi":"10.1002/j.2168-0159.2014.tb00042.x","title":"13.2: <i>Invited Paper</i> : LTPS vs Oxide Backplanes for AMOLED Displays: System Design Considerations and Compensation Techniques","year":2014,"lang":"en","type":"article","venue":"SID Symposium Digest of Technical Papers","topic":"Thin-Film Transistor Technologies","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"IGNIS Innovation (Canada)","funders":"","keywords":"Backplane; AMOLED; Compensation (psychology); Materials science; Electronic engineering; Computer science; Optoelectronics; Electrical engineering; Thin-film transistor; Engineering; Nanotechnology; Active matrix","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":[0.000703681,0.0003337522,0.0002169708,0.0003093675,0.000840661,0.002151711,0.0004894436,0.001129407,0.01514025],"category_scores_gemma":[0.0005205384,0.00017949,0.0002483274,0.0002657099,0.0002979289,0.0007002215,0.0002485775,0.0007223602,0.00374164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009511219,"about_ca_system_score_gemma":0.0003718327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000880566,"about_ca_topic_score_gemma":0.002901797,"domain_scores_codex":[0.9997304,0.00004330246,0.000008321172,0.00004887682,0.0001235901,0.00004551052],"domain_scores_gemma":[0.9997482,0.00005628025,0.00001209082,0.00001247781,0.0001401425,0.00003089187],"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":[0.001566327,0.0001889657,0.001940165,0.0009571042,0.00005804585,0.002034801,0.0004763724,0.003191909,0.5021367,0.03752007,0.2653178,0.1846118],"study_design_scores_gemma":[0.00009886536,0.00148584,0.003042239,0.0001249087,0.00007210198,0.001022757,0.0003446355,0.01054647,0.2930215,0.004299917,0.6858624,0.00007830089],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"methods","genre_scores_codex":[0.1975899,0.0219567,0.16109,0.02972663,0.02571644,0.001295405,0.001539401,0.003766073,0.5573194],"genre_scores_gemma":[0.4547143,0.007069988,0.0370084,0.003249291,0.005217158,0.0001744106,0.0006503164,0.0006154512,0.4913008],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01514025,"threshold_uncertainty_score":0.05064923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009189692695971457,"score_gpt":0.1976753715559427,"score_spread":0.1884856788599713,"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."}}