{"id":"W2018245402","doi":"10.1016/j.procir.2014.03.117","title":"Dual-Channel Supply Coordination in Online Shopping","year":2014,"lang":"en","type":"article","venue":"Procedia CIRP","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dual (grammatical number); Order (exchange); Product (mathematics); Supply chain; Channel (broadcasting); Center (category theory); Business; Computer science; Order fulfillment; Operations research; Marketing; Telecommunications; Engineering; 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":[],"consensus_categories":[],"category_scores_codex":[0.0004484039,0.0001649188,0.0001713065,0.0004282057,0.00008616535,0.0001543283,0.0001662449,0.00005729911,0.000196774],"category_scores_gemma":[0.0002654974,0.0001691929,0.00004827443,0.000459077,0.00002747258,0.000795415,0.0001492046,0.000121482,0.0002759497],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004743612,"about_ca_system_score_gemma":0.000009379588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001373783,"about_ca_topic_score_gemma":0.0002705037,"domain_scores_codex":[0.9988566,0.000008777944,0.0002697153,0.0003081991,0.0002374714,0.0003192452],"domain_scores_gemma":[0.9995726,0.00002806945,0.0001206431,0.0001739541,0.0000903906,0.0000143542],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001572945,0.00180412,0.3592963,0.003120959,0.00009994025,0.00006138976,0.001024632,0.002762366,0.001876788,0.2861965,0.2367106,0.1068892],"study_design_scores_gemma":[0.002611327,0.00003634769,0.09934122,0.0002708716,0.00005398792,0.000002789144,0.001243475,0.257214,0.00008435996,0.01875782,0.6195377,0.0008461725],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8662155,0.0001848702,0.00275729,0.02230669,0.002279328,0.001195131,0.000005161584,0.0006187335,0.1044373],"genre_scores_gemma":[0.9929424,0.000005951726,0.00008981693,0.003982875,0.001748296,0.00005466341,0.0001107737,0.00002892238,0.001036331],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.382827,"threshold_uncertainty_score":0.6899486,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.019309692877423,"score_gpt":0.2208652822813205,"score_spread":0.2015555894038975,"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."}}