{"id":"W2571824574","doi":"10.2139/ssrn.2867528","title":"Forecasting Product Life Cycle Curves: Practical Approach and Empirical Analysis","year":2016,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Innovation Diffusion and Forecasting","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Product lifecycle; Econometrics; Product (mathematics); New product development; Economics; Computer science; Mathematics; Management","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00316172,0.0006305397,0.0006127467,0.001895012,0.0003109928,0.001300955,0.0007816316,0.00139839,0.002790011],"category_scores_gemma":[0.02742058,0.0003205323,0.0006189859,0.002649653,0.0003317578,0.002429439,0.0004387291,0.0009925044,0.0005273535],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009865494,"about_ca_system_score_gemma":0.0006774911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01153734,"about_ca_topic_score_gemma":0.005942044,"domain_scores_codex":[0.9994529,0.0002699938,0.00002770543,0.0000848591,0.0001269664,0.00003748255],"domain_scores_gemma":[0.9866524,0.01115488,0.0003957674,0.0007394241,0.0009348409,0.0001226459],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001139144,0.00009125443,0.0353849,0.0001367252,0.00005127959,0.00007707557,0.0002447547,0.8263832,0.001061586,0.01585094,0.001224958,0.1193795],"study_design_scores_gemma":[0.000003994718,0.00002810068,0.003531829,0.00001143118,0.000009281081,0.00002131082,0.00004977164,0.9886613,0.000331703,0.006927315,0.0004151715,0.000008892954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5910611,0.0005981483,0.398671,0.0006205944,0.00003230936,0.0002044741,0.00110222,0.0004032941,0.007306833],"genre_scores_gemma":[0.9556373,0.0003471162,0.0420602,0.00002117453,0.00001837161,0.00005158816,0.0007127125,0.0000438092,0.001107761],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01153734,"threshold_uncertainty_score":0.0229404,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1407544715127171,"score_gpt":0.3935762190294603,"score_spread":0.2528217475167432,"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."}}