{"id":"W1517483050","doi":"10.20381/ruor-2621","title":"The Connections between R&amp;D Intensity and Time in Industrial Innovation","year":2012,"lang":"en","type":"article","venue":"uO Research (University of Ottawa)","topic":"Economic Growth and Productivity","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Intensity (physics); Computer science; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002786279,0.0002384089,0.0005062324,0.003143029,0.0006786954,0.001957804,0.0007480604,0.0008652585,0.004613919],"category_scores_gemma":[0.02089244,0.0002613936,0.0007721022,0.004607479,0.003100313,0.002524971,0.002042414,0.001236633,0.0004471649],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002018013,"about_ca_system_score_gemma":0.0008450203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008016486,"about_ca_topic_score_gemma":0.007182282,"domain_scores_codex":[0.9983662,0.0004066767,0.0001501374,0.0003717732,0.0003611224,0.0003441317],"domain_scores_gemma":[0.9644287,0.02295766,0.00813265,0.001424177,0.001057822,0.001998976],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006518007,0.0002507192,0.7304506,0.0002909445,0.0003773358,0.0006061497,0.003188698,0.02645364,0.004519975,0.1312792,0.0009473104,0.1009837],"study_design_scores_gemma":[0.00003884635,0.0002077758,0.9108585,0.00007320921,0.000128336,0.0003520375,0.0008631906,0.007740018,0.00118472,0.07247474,0.005988654,0.00008996509],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.959021,0.005072623,0.01692504,0.002167906,0.00003036961,0.00002692015,0.0005152256,0.0000667649,0.01617404],"genre_scores_gemma":[0.996493,0.0008736724,0.001073053,0.00007221328,0.00006156886,0.00001253867,0.000111507,0.000009508714,0.001292943],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008016486,"threshold_uncertainty_score":0.01593965,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2090846868348989,"score_gpt":0.2838098612245317,"score_spread":0.0747251743896328,"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."}}