{"id":"W2886381420","doi":"10.14510/araj.2017.4120","title":"THE CIRCUMSTANCES AND CONTRIBUTIVE FACTORS OF ESTIMATE AND SELECTION OF THE INFORMATIONAL SITUATIONAL RESOURCES OF ECONOMIC INFORMATICS SYSTEMS","year":2017,"lang":"en","type":"article","venue":"Journal of the American Romanian Academy of Arts and Sciences","topic":"Enterprise Management and Information Systems","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Selection (genetic algorithm); Informatics; Situational ethics; Operations research; Knowledge management; Computer science; Psychology; Political science; Engineering; Social psychology; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.006214695,0.0003174071,0.0003014949,0.004792428,0.002080929,0.008966473,0.0004441582,0.0008580764,0.003242986],"category_scores_gemma":[0.02448055,0.0004454322,0.0003291458,0.00259689,0.005623915,0.005718227,0.002395054,0.0009175788,0.0003633701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002383356,"about_ca_system_score_gemma":0.003415005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001284032,"about_ca_topic_score_gemma":0.001202948,"domain_scores_codex":[0.990733,0.004588748,0.0007225457,0.0009189866,0.002288809,0.0007477356],"domain_scores_gemma":[0.9852324,0.008092375,0.002327297,0.001220491,0.002312947,0.0008146496],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002652963,0.0001620446,0.1203102,0.0003149415,0.00006473692,0.001217739,0.01311048,0.00289089,0.003117723,0.7737991,0.001770466,0.0829763],"study_design_scores_gemma":[0.00007717425,0.0003204714,0.2760119,0.000775045,0.000276154,0.003718105,0.04801878,0.01954469,0.01348471,0.5713383,0.06618857,0.0002460441],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7284527,0.002214096,0.08772316,0.005019724,0.00008807243,0.0003276739,0.0005447783,0.0001692272,0.1754607],"genre_scores_gemma":[0.9924856,0.0003351348,0.00571241,0.00003788045,0.00005062197,0.00006224204,0.00006320207,0.00001426749,0.001238567],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008966473,"threshold_uncertainty_score":0.03286684,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01939853806653375,"score_gpt":0.2660545897297308,"score_spread":0.246656051663197,"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."}}