{"id":"W4409605163","doi":"10.61091/jcmcc127b-307","title":"Study on Constructing the Optimization of Omni-Channel Marketing Resource Allocation Based on Dynamic Planning Algorithm in the Era of Digital Transformation","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"E-commerce and Technology Innovations","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Transformation (genetics); Channel (broadcasting); Computer science; Resource allocation; Resource (disambiguation); Digital transformation; Algorithm; Operations research; Telecommunications; Engineering; World Wide Web; Computer network","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003051469,0.0001686905,0.0003818894,0.0004701595,0.0002547867,0.0001826234,0.0003793993,0.00009579623,8.753076e-7],"category_scores_gemma":[0.0007933751,0.0001188286,0.00007993446,0.0009816354,0.0001039424,0.0002951733,0.00006729006,0.0005487067,1.757128e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003670832,"about_ca_system_score_gemma":0.00004616487,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000785004,"about_ca_topic_score_gemma":3.772113e-7,"domain_scores_codex":[0.9981012,0.00008012425,0.001099489,0.0001179466,0.0004522357,0.000149015],"domain_scores_gemma":[0.9967377,0.001342556,0.001322785,0.00019794,0.0003929473,0.000006025113],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004825142,0.002959829,0.004518381,0.0006208983,0.0001950803,0.00001130817,0.004521562,0.05303276,0.00005756554,0.9117385,0.00009746942,0.02176413],"study_design_scores_gemma":[0.008550476,0.0006532538,0.002326824,0.003033344,0.0002569417,0.00001195694,0.05502627,0.7764451,0.0001180319,0.1531818,0.00008757802,0.0003083898],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9625974,0.00002980697,0.02992753,0.001553183,0.001842635,0.0006547405,0.000001557179,0.00002335156,0.003369797],"genre_scores_gemma":[0.9991205,0.000001327819,0.0005923922,0.0000928794,0.0001724314,0.000003530972,0.000004693632,0.00001175115,4.553821e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7585567,"threshold_uncertainty_score":0.484569,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01169511928271707,"score_gpt":0.2476811563394165,"score_spread":0.2359860370566994,"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."}}