{"id":"W2159879347","doi":"10.1017/s136510051000012x","title":"CAPITALIZING R&D EXPENDITURES","year":2010,"lang":"en","type":"article","venue":"Macroeconomic Dynamics","topic":"Economic Growth and Productivity","field":"Economics, Econometrics and Finance","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Statistics Canada; University of British Columbia","funders":"","keywords":"Depreciation (economics); Obsolescence; Economics; Context (archaeology); Asset (computer security); Matching (statistics); Microeconomics; Computer science; Business; Marketing; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006498997,0.0002752084,0.0004954932,0.000241687,0.0001886629,0.0001792023,0.0005081631,0.000205611,0.002581401],"category_scores_gemma":[0.00008618223,0.0003628055,0.0001903853,0.00005817834,0.0001534172,0.0004526918,0.0001341377,0.0004988028,0.005766314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001797885,"about_ca_system_score_gemma":0.00003034123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005164313,"about_ca_topic_score_gemma":0.002936656,"domain_scores_codex":[0.9979568,0.00001022686,0.0007163757,0.0007572675,0.00001737209,0.00054201],"domain_scores_gemma":[0.9985179,0.00005827034,0.0003877772,0.0008346174,0.00001296037,0.0001885244],"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":[0.00001309583,0.00005925809,0.154461,0.00002054396,0.00005424243,0.000002449019,0.000214024,0.00005871978,0.000244792,0.8402286,0.002372338,0.002271062],"study_design_scores_gemma":[0.001141907,0.00004519706,0.04226224,0.000006456778,0.00001220305,0.0000924609,0.0001357951,0.01539626,0.0003201292,0.4566703,0.4825346,0.001382457],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8777708,0.0002877614,0.001868158,0.0009973657,0.003721883,0.000197853,0.0003747716,0.0001250787,0.1146563],"genre_scores_gemma":[0.9919438,0.00003779443,0.00268142,0.0003846544,0.0005630576,0.00003405884,0.0001261302,0.00006208693,0.004166975],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4801623,"threshold_uncertainty_score":0.9998824,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01677256577530079,"score_gpt":0.2173141395649419,"score_spread":0.2005415737896411,"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."}}