{"id":"W6968782437","doi":"10.5281/zenodo.5642796","title":"Material Growth and Material Characterization for Conventional & Nanoelectronic Devices Applications: An Innovative Prospective","year":2021,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advancements in Semiconductor Devices and Circuit Design","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nexen (Canada)","funders":"","keywords":"Characterization (materials science); Software; Software development; Electronics; Electronic materials","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001567892,0.0001254718,0.0001217422,0.00008045455,0.0006837652,0.0005041808,0.0002195729,0.00005275796,0.002350513],"category_scores_gemma":[0.00004514274,0.0001457151,0.00001675503,0.000334742,0.00006209408,0.0004676759,0.0001108611,0.00008307037,0.0001088329],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009625281,"about_ca_system_score_gemma":0.000005559535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":9.488243e-7,"about_ca_topic_score_gemma":2.891055e-7,"domain_scores_codex":[0.9990384,0.00007125516,0.0002123713,0.0003101183,0.0001378035,0.0002300748],"domain_scores_gemma":[0.9991111,0.000009678147,0.0000655898,0.0001690175,0.0005823786,0.00006222946],"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":[0.00005907308,0.00008047171,0.00001478585,0.0002428348,0.00008128592,0.000001044186,0.0004364462,0.00001797812,0.9529172,0.02515302,0.000662585,0.02033333],"study_design_scores_gemma":[0.0008110252,0.0001786047,0.001223329,0.00003431973,0.00002585688,0.00007492845,0.000211478,0.0003404652,0.1151156,0.001810822,0.8798258,0.0003477959],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7316756,0.0001692722,0.2521661,0.0001318937,0.0005969563,0.002519196,0.003518356,0.001675552,0.007547138],"genre_scores_gemma":[0.9792359,0.0001126007,0.00101313,0.00006470499,0.0003556739,0.000002816764,0.01843929,0.0006396153,0.0001362466],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8791632,"threshold_uncertainty_score":0.9985615,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02314068190955915,"score_gpt":0.2438596428473584,"score_spread":0.2207189609377992,"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."}}