{"id":"W4395010296","doi":"10.1002/nav.22190","title":"Forecasting using reference prices with exposure effect","year":2024,"lang":"en","type":"article","venue":"Naval Research Logistics (NRL)","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Econometrics; Economics; Computer science; Statistics; Mathematics","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":["metaresearch","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.01109209,0.0002001628,0.0002812141,0.0005567918,0.0006136097,0.001329218,0.001188059,0.0001574946,0.0001417328],"category_scores_gemma":[0.01270505,0.0001219801,0.00007302478,0.002771016,0.0006068863,0.0002287035,0.0004846806,0.00101388,0.0002285098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001641311,"about_ca_system_score_gemma":0.0003794099,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002534261,"about_ca_topic_score_gemma":0.00007762186,"domain_scores_codex":[0.9946567,0.0003531467,0.0004966754,0.0008439464,0.00292803,0.0007215334],"domain_scores_gemma":[0.9888138,0.008958108,0.0001001348,0.0008338928,0.001079508,0.000214604],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004933574,0.0002388427,0.01167238,0.0005366536,0.0001217881,0.001069548,0.0009996741,0.004551094,0.01282112,0.3143317,0.03693036,0.6162335],"study_design_scores_gemma":[0.0003289671,0.002760643,0.0007765298,0.0008692403,0.00004452328,0.0002443998,0.0003675375,0.7016068,0.005982227,0.1933025,0.09312995,0.0005867494],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5296095,0.001313218,0.3857313,0.0008435744,0.000313442,0.001549149,0.0001438584,0.0007334286,0.07976257],"genre_scores_gemma":[0.9637225,0.0000203222,0.03464551,0.00001399394,0.0002115122,0.00007422001,0.000008593355,0.0000323518,0.001270986],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6970557,"threshold_uncertainty_score":0.9997075,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7213806515626001,"score_gpt":0.5756521973754204,"score_spread":0.1457284541871797,"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."}}