{"id":"W3117926612","doi":"10.51130/graphicon-2020-2-3-77","title":"Intelligent Analysis of the Ecological State of Environment with Application of Distributed Expertise (on the Example of Bryansk Region)","year":2020,"lang":"en","type":"article","venue":"Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 2","topic":"Economic and Technological Systems Analysis","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Cytodiagnostics (Canada)","funders":"Russian Foundation for Basic Research","keywords":"Consistency (knowledge bases); Reliability (semiconductor); Computer science; Pollution; Estimation; Environmental pollution; Construct (python library); Control (management); Risk analysis (engineering); Environmental science; Environmental resource management; Ecology; Environmental protection; Systems engineering; Artificial intelligence; Engineering; Business","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003782015,0.000248254,0.0003394555,0.0008531753,0.0004182662,0.0007983236,0.0003517396,0.0003793287,0.0008650754],"category_scores_gemma":[0.0006798889,0.0001223301,0.0004173343,0.0009019575,0.0004233923,0.0005519505,0.0008410127,0.000218329,0.00008238419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007372694,"about_ca_system_score_gemma":0.0004715765,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02293896,"about_ca_topic_score_gemma":0.01478046,"domain_scores_codex":[0.9997577,0.0000783248,0.000008051228,0.0000746275,0.00004058333,0.00004065507],"domain_scores_gemma":[0.99983,0.00006430844,0.00003452085,0.0000180179,0.00003529896,0.00001781858],"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":[0.0007695628,0.0001498941,0.07692493,0.0006089513,0.0002645014,0.005889846,0.004541019,0.5657796,0.07389376,0.04124438,0.001574187,0.2283594],"study_design_scores_gemma":[0.00004673439,0.0002061861,0.09605343,0.00004277148,0.0001700003,0.0009424429,0.002289263,0.8597789,0.01194832,0.02351967,0.004922984,0.00007943982],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8630608,0.0003205503,0.1288794,0.0002406631,0.000008198609,0.00003794775,0.0001609052,0.0001528863,0.007138554],"genre_scores_gemma":[0.976555,0.0001052461,0.0223179,0.000005177684,0.000003213309,0.00001012784,0.00005746249,0.000007059447,0.0009387769],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02293896,"threshold_uncertainty_score":0.0456109,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03291986499374822,"score_gpt":0.217704733938349,"score_spread":0.1847848689446008,"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."}}