{"id":"W4395013599","doi":"10.33042/2522-1809-2024-1-182-14-19","title":"LIQUID NEURAL NETWORKS: PRINCIPLE OF WORK AND AREAS OF APPLICATION","year":2024,"lang":"en","type":"article","venue":"Municipal economy of cities","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Technische Universität Wien; Universität Wien; Institute for Catastrophic Loss Reduction","keywords":"Work (physics); Artificial neural network; Computer science; Artificial intelligence; Engineering; Mechanical engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.001608469,0.0009721789,0.0007299912,0.001586178,0.0006300329,0.003273729,0.001755266,0.002861818,0.003707517],"category_scores_gemma":[0.003249602,0.0004492311,0.0006971154,0.00182134,0.002932596,0.004041784,0.001871948,0.002194909,0.001554339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001315953,"about_ca_system_score_gemma":0.001153252,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001438267,"about_ca_topic_score_gemma":0.000640762,"domain_scores_codex":[0.9986188,0.0004040046,0.00008699165,0.0002706354,0.0005509668,0.00006854568],"domain_scores_gemma":[0.9991128,0.0004336451,0.000070751,0.00008996006,0.0002453001,0.00004746973],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00009369801,0.00008847593,0.001112318,0.0008933146,0.00006786513,0.0002282142,0.0003005793,0.03674075,0.003502396,0.5163317,0.009954536,0.4306863],"study_design_scores_gemma":[0.000030379,0.0001878906,0.0006871906,0.001038468,0.00006068899,0.0008484434,0.0002353331,0.2189042,0.004369512,0.5372648,0.2362873,0.00008588216],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.003917198,0.04152372,0.8794335,0.00539811,0.0007761545,0.0002101618,0.0001342345,0.0006237415,0.0679833],"genre_scores_gemma":[0.2620744,0.0805777,0.6075963,0.003182373,0.002615216,0.001276131,0.0004193317,0.0003792873,0.04187927],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.003707517,"threshold_uncertainty_score":0.01240289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01203822475960331,"score_gpt":0.2477267024797826,"score_spread":0.2356884777201793,"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."}}