{"id":"W6988406127","doi":"","title":"Підвищення ефективності технологічних процесів складання шляхом формалізованого аналізу конструкцій виробів","year":2022,"lang":"uk","type":"dissertation","venue":"Електронний архів наукових та освітніх матеріалів КПІ ім. Ігоря Сікорського (КПІ ім. Ігоря Сікорського)","topic":"Advanced Scientific Research Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Identification (biology); Process (computing); Product (mathematics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004004079,0.0009114493,0.0003960751,0.00198591,0.006156927,0.01423718,0.001744396,0.003420892,0.08309919],"category_scores_gemma":[0.009611258,0.0008412629,0.0008731859,0.002014715,0.007813955,0.008966939,0.003925946,0.003950982,0.02969979],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00680816,"about_ca_system_score_gemma":0.009167872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02482178,"about_ca_topic_score_gemma":0.03940732,"domain_scores_codex":[0.9945688,0.001576771,0.0003158575,0.0008189033,0.002077654,0.0006420438],"domain_scores_gemma":[0.9936441,0.002148894,0.0003472776,0.0009037818,0.00231533,0.0006405314],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007905943,0.00006742221,0.002695632,0.0002469749,0.00001855483,0.0004059814,0.007484926,0.0005451934,0.001367541,0.7239449,0.1663922,0.09675162],"study_design_scores_gemma":[0.00001865859,0.00002935675,0.002622083,0.0002616132,0.00002612473,0.0002791848,0.006489466,0.0008861222,0.001751941,0.1011867,0.8863881,0.00006057267],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.008760353,0.002201586,0.0348271,0.03917432,0.001918989,0.0001679529,0.0007620183,0.000681931,0.9115058],"genre_scores_gemma":[0.285367,0.005842192,0.05302718,0.01270771,0.001278869,0.0007500389,0.0009015101,0.001141161,0.6389843],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.08309919,"threshold_uncertainty_score":0.2779945,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07862262011947428,"score_gpt":0.4247600877438849,"score_spread":0.3461374676244106,"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."}}