{"id":"W2323364057","doi":"10.1557/proc-1030-g07-07","title":"Poly and Nano-crystalline High Electron Mobility Thin Film Transistors on Plastic Substrates for Large Area Applications","year":2007,"lang":"en","type":"article","venue":"MRS Proceedings","topic":"Thin-Film Transistor Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Acadia University","funders":"Iran National Science Foundation","keywords":"Materials science; Thin-film transistor; Crystallization; Amorphous silicon; Crystallinity; Polyethylene terephthalate; Silane; Silicon; Optoelectronics; Nanocrystalline silicon; Oxide thin-film transistor; Amorphous solid; Scanning electron microscope; Thin film; Chemical engineering; Layer (electronics); Silicon oxide; Electron mobility; Nanotechnology; Composite material; Crystalline silicon; Crystallography; Silicon nitride","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002680721,0.000264033,0.0002423717,0.0001765461,0.0001452266,0.00003989103,0.0001969974,0.0002205332,0.00001018815],"category_scores_gemma":[0.00005673077,0.0002663234,0.00006210405,0.0002784065,0.00007122762,0.0001450015,0.000008985616,0.0002730151,0.000003258572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001632496,"about_ca_system_score_gemma":0.0000119233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006189997,"about_ca_topic_score_gemma":0.00005260516,"domain_scores_codex":[0.9986705,0.000001266096,0.0003030793,0.0003576494,0.0001499672,0.0005175111],"domain_scores_gemma":[0.9995373,0.0001360505,0.00004309463,0.0001274402,0.0000738209,0.00008225098],"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":[0.0006218402,0.0006767844,0.002551695,0.002465888,0.0002566583,0.000003580936,0.002432385,0.001478845,0.8538963,0.1289754,0.00356985,0.003070712],"study_design_scores_gemma":[0.002092559,0.0007692116,0.01313221,0.0001256917,0.000168831,0.00001636316,0.00103954,0.01005169,0.9191662,0.008083982,0.04416879,0.001184933],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.975978,0.001303788,0.01965223,0.0001270022,0.00008481018,0.0007498182,0.0000999454,0.001653528,0.0003508512],"genre_scores_gemma":[0.9972844,0.00004457782,0.002188839,0.00003519069,0.00004847925,0.0002660498,0.00002432914,0.00005727996,0.00005092538],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1208915,"threshold_uncertainty_score":0.9999789,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007385058580853474,"score_gpt":0.2065255757526679,"score_spread":0.1991405171718145,"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."}}