{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008154192,0.00008178447,0.0001880308,0.00007370515,0.00001031617,0.000008757134,0.000126604,0.00004483065,0.000006944526],"category_scores_gemma":[0.00000720473,0.00008753268,0.00002627605,0.00009177978,0.0001397765,0.0002001273,0.0000624236,0.00007293426,2.005568e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001439699,"about_ca_system_score_gemma":0.000007147236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008867512,"about_ca_topic_score_gemma":0.000005353099,"domain_scores_codex":[0.9995199,0.000005859875,0.0002646883,0.00008951139,0.00002976258,0.00009027213],"domain_scores_gemma":[0.9996389,0.00006638449,0.00005123853,0.0002044292,0.00002026515,0.00001880441],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000305205,0.00007935904,0.004799884,0.008191677,0.0004418692,0.000003647089,0.006311267,0.5258749,0.002689457,0.3498514,0.0008355109,0.1006159],"study_design_scores_gemma":[0.0001747718,0.0001802402,0.0005537187,0.0006362579,0.00004098553,0.00000403624,0.0003202251,0.9071922,0.04367296,0.01301572,0.03387926,0.0003296092],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8915231,0.00862047,0.09546719,0.00004117405,0.00007467962,0.0001831478,0.00003384103,0.0003433839,0.003713047],"genre_scores_gemma":[0.9968324,0.0001600692,0.002902941,0.000005673304,0.0000254127,0.00002818708,0.00001560599,0.00001775793,0.00001194886],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3813173,"threshold_uncertainty_score":0.3569479,"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."}}