{"id":"W3123112878","doi":"","title":"Measuring Innovation in Canada: The Tale Told by Patent Applications","year":2014,"lang":"en","type":"article","venue":"e-briefs","topic":"Canadian Policy and Governance","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Lagging; Per capita; Economics; Business; Economic history; Sociology; Demography","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":[0.0003289441,0.00003776293,0.00004760379,0.00001880132,0.0002249188,0.00002532158,0.0001580035,0.00002117364,0.00004354959],"category_scores_gemma":[0.0001411235,0.00003410368,0.000008488746,0.0004578806,0.00004876062,0.0000564751,0.00001074006,0.00007159371,0.00001454118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004722347,"about_ca_system_score_gemma":0.0007391365,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9966366,"about_ca_topic_score_gemma":0.9989492,"domain_scores_codex":[0.9994013,0.00004110689,0.0001199542,0.00008986771,0.0001704152,0.0001773304],"domain_scores_gemma":[0.9997065,0.00004745097,0.00005885272,0.0001147449,0.00003268899,0.00003972647],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000002599975,0.00001622687,0.01033536,0.000006828189,0.000005324749,6.963206e-7,0.003690872,0.00003938006,0.0002628453,0.3561626,0.5947276,0.03474965],"study_design_scores_gemma":[0.00005614205,0.000001455198,0.02091889,0.000004500935,8.346504e-7,1.271712e-7,0.0001685595,0.00001651885,0.0001389479,0.0009593432,0.9776857,0.00004901206],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6693519,0.0004190874,0.001431162,0.1688844,0.0003927505,0.001004015,0.0002375434,0.00004957162,0.1582295],"genre_scores_gemma":[0.991206,0.00001117548,0.00000793826,0.004900012,0.0001516302,0.00005261468,0.000003658708,0.000003552635,0.003663428],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3829581,"threshold_uncertainty_score":0.1729917,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03773040857452521,"score_gpt":0.2339672952237808,"score_spread":0.1962368866492556,"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."}}