{"id":"W2755612177","doi":"10.17748/2075-9908-2017-9-4/1-92-96","title":"THE HISTORICAL CONDITIONS OF DEVELOPMENT OF THE MINING INDUSTRY IN THE URALS IN THE XVIII CENTURY","year":2017,"lang":"en","type":"article","venue":"Historical and social-educational ideas","topic":"Engineering and Environmental Studies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Modernization theory; Government (linguistics); Quarter (Canadian coin); Population; State (computer science); Empire; Business; Mining industry; Economy; Economic growth; Geography; Political science; Engineering; Archaeology; Economics; Mining engineering; Sociology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002467282,0.00007112785,0.0001033853,0.00001607411,0.0006333878,0.00001257977,0.000271278,0.000078072,0.000003860137],"category_scores_gemma":[0.00007582109,0.00003718762,0.0000370739,0.00006376168,0.0001091224,0.00003078523,0.00003677064,0.0002752611,7.063569e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006330637,"about_ca_system_score_gemma":0.00003352457,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001447137,"about_ca_topic_score_gemma":0.00008094862,"domain_scores_codex":[0.9993976,0.0000375821,0.0002094462,0.00006265296,0.0001862298,0.0001065212],"domain_scores_gemma":[0.9995607,0.0002339793,0.00005888861,0.0001248369,0.000009322945,0.00001232787],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003025796,0.001220259,0.3452727,0.0003770925,0.0002784613,0.000004362304,0.3030569,0.00171087,0.001435485,0.1202767,0.2006778,0.025659],"study_design_scores_gemma":[0.00007963715,0.000003699422,0.9016432,0.00001590556,0.000007522735,0.000001161334,0.001811326,0.00001361148,0.0000105,0.000617921,0.09573891,0.00005659814],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9822695,0.002730214,0.000005720879,0.01052496,0.0006038095,0.0001090335,0.000004374916,0.000005401679,0.003747051],"genre_scores_gemma":[0.999355,0.00009427297,0.00004338114,0.00004285491,0.00008978685,0.00004963754,0.000001989208,0.000004640233,0.0003184901],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5563704,"threshold_uncertainty_score":0.487157,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02205405499650801,"score_gpt":0.2380351531333362,"score_spread":0.2159810981368282,"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."}}