{"id":"W2755503889","doi":"10.1504/ijtip.2017.10006430","title":"Using patents and innovation strings to anticipate the next Kondratieff long waves","year":2017,"lang":"en","type":"article","venue":"International Journal of Technology Intelligence and Planning","topic":"Economic Development and Digital Transformation","field":"Economics, Econometrics and Finance","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Kondratiev wave; Analogy; Quarter (Canadian coin); Economics; Work (physics); Keynesian economics; Physics; History","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":[0.0003436547,0.00006794625,0.000131374,0.0006281884,0.0001565304,0.0003303569,0.0003367425,0.00005783518,0.00001084386],"category_scores_gemma":[0.0002151612,0.00005892762,0.00001780459,0.00008530812,0.00007763653,0.0008630547,0.00008218753,0.0001265908,0.000008758814],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002559471,"about_ca_system_score_gemma":0.00001100183,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001271307,"about_ca_topic_score_gemma":0.000001240035,"domain_scores_codex":[0.9992848,0.000002052454,0.0004928386,0.00009291506,0.00003440506,0.00009299123],"domain_scores_gemma":[0.9992099,0.00002226229,0.0005480374,0.00007748674,0.0001216781,0.00002065199],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004739377,0.00001856934,0.8003045,0.000009976019,0.0001433879,0.00002890725,0.001680377,0.0004553591,0.0002416538,0.1079345,0.00003837258,0.08909702],"study_design_scores_gemma":[0.001149834,0.000445766,0.4966178,0.001255097,0.00003463445,0.001006558,0.004882414,0.02802858,0.02298763,0.4312238,0.01146401,0.000903875],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.972831,0.0003372217,0.02277624,0.001962405,0.0005113372,0.00005100357,0.000006199462,0.000005655946,0.001518939],"genre_scores_gemma":[0.9980468,0.0001865513,0.001506558,0.0001459771,0.00005547476,9.717506e-7,0.000001349227,0.000003995575,0.00005238148],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3232893,"threshold_uncertainty_score":0.3185639,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2250208713867384,"score_gpt":0.338669033423643,"score_spread":0.1136481620369046,"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."}}