{"id":"W3014198157","doi":"10.22215/timreview/1340","title":"The Effect of Machine Learning on Knowledge-Intensive R&D in the Technology Industry","year":2020,"lang":"en","type":"article","venue":"Technology Innovation Management Review","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001278629,0.0002306683,0.0003697692,0.0008463128,0.0001970981,0.00005043255,0.001172132,0.0002288876,0.00008342525],"category_scores_gemma":[0.001659354,0.0001308282,0.00004435564,0.01173538,0.0002577869,0.0001911242,0.0005130843,0.001094138,0.0004239848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000197002,"about_ca_system_score_gemma":0.000006348484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001042137,"about_ca_topic_score_gemma":0.00001093738,"domain_scores_codex":[0.9985182,0.00004339896,0.0006308616,0.0003299584,0.0002176048,0.0002600262],"domain_scores_gemma":[0.9984751,0.0001041103,0.0005050011,0.0005494749,0.0003632195,0.000003031935],"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":[0.00004039312,0.00003810539,0.01870005,0.003687731,0.00006116925,0.00001255304,0.00001176103,0.00001509802,0.0000813474,0.5686777,0.02524606,0.383428],"study_design_scores_gemma":[0.0002766498,0.00007578793,0.0004867253,0.001593214,0.00009328117,0.000003713507,0.0001759735,0.0002316796,0.0001962558,0.002504151,0.9941967,0.0001658431],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1577585,0.06279342,0.00246825,0.7264045,0.0006969067,0.006756075,0.000006405036,0.001393962,0.04172189],"genre_scores_gemma":[0.9722954,0.008649273,0.00007255888,0.01801417,0.0001641391,0.0003782923,0.00009097846,0.00003261681,0.0003025815],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9689507,"threshold_uncertainty_score":0.5638461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06291261611421814,"score_gpt":0.3317062876184411,"score_spread":0.268793671504223,"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."}}