{"id":"W2604872011","doi":"10.1002/bdm.559","title":"Calibration accuracy of a judgmental process that predicts the commercial success of new product ideas","year":2007,"lang":"en","type":"preprint","venue":"Journal of Behavioral Decision Making","topic":"Innovation Diffusion and Forecasting","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Commercialization; Predictability; Product (mathematics); Aggregate (composite); New product development; Process (computing); Order (exchange); Computer science; Calibration; Econometrics; Economics; Marketing; Business; Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.009228428,0.0003912406,0.001153762,0.001514028,0.0002324734,0.0004594814,0.002669287,0.0003361896,0.00048193],"category_scores_gemma":[0.005547782,0.0002385273,0.0006043006,0.001652805,0.0002336729,0.00100046,0.001111344,0.0013668,0.000003474082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001176637,"about_ca_system_score_gemma":0.001031115,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007099651,"about_ca_topic_score_gemma":0.00006751849,"domain_scores_codex":[0.9881441,0.0002939264,0.004608366,0.0005135038,0.006101811,0.0003383005],"domain_scores_gemma":[0.9823405,0.002123886,0.01116641,0.0008833322,0.003329857,0.0001560281],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001582729,0.0006072253,0.1181886,0.00007601589,0.00004646393,0.00005864288,0.00549379,0.005343327,0.001164474,0.0002623393,0.009475758,0.8577006],"study_design_scores_gemma":[0.007580944,0.001884956,0.7085023,0.01586159,0.001123413,0.001026608,0.01639424,0.02374802,0.04773864,0.1702282,0.003808585,0.002102484],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9522977,0.0003687908,0.04234885,0.0003269974,0.003808629,0.0005810935,0.00004266461,0.00001236159,0.000212888],"genre_scores_gemma":[0.9936001,0.00002200214,0.00546379,0.0001344883,0.0006298003,0.000003055515,0.000007278602,0.00003241441,0.000107078],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8555982,"threshold_uncertainty_score":0.9726862,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2580742075903589,"score_gpt":0.4807887113451321,"score_spread":0.2227145037547732,"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."}}