{"id":"W7132326619","doi":"","title":"Freshippo: Can a New Retail Species Gain Competitive Edge with Digital Intelligence?","year":2023,"lang":"","type":"other","venue":"CEIBS Institutional Repository","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Casa","funders":"","keywords":"Competitive advantage; Enhanced Data Rates for GSM Evolution; Competition (biology); Key (lock)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","sts","research_integrity","insufficient_payload"],"category_scores_codex":[0.0005300582,0.002687071,0.002118419,0.001775425,0.002091589,0.001785783,0.002225906,0.001471118,0.004635353],"category_scores_gemma":[0.001199143,0.002597825,0.0009319168,0.002576839,0.01012694,0.001234571,0.0008744631,0.002722858,0.02968125],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.006566901,"about_ca_system_score_gemma":0.01942324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001340827,"about_ca_topic_score_gemma":0.00740193,"domain_scores_codex":[0.9878764,0.0003713022,0.002180195,0.003440296,0.004030176,0.002101647],"domain_scores_gemma":[0.9921447,0.0008188524,0.001846051,0.002205385,0.001108847,0.001876134],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003111836,0.002124883,0.01189792,0.0008071347,0.007683984,0.02814601,0.004542269,0.007719629,0.001474408,0.7042074,0.2187837,0.009500854],"study_design_scores_gemma":[0.001315643,0.0007082357,0.00393955,0.008461249,0.0005066004,0.002912768,0.002571063,0.000176106,0.002552865,0.000987881,0.9723391,0.003528926],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.001082772,0.003025323,0.002913971,0.000285851,0.007660746,0.002222528,0.004127121,0.001943933,0.9767377],"genre_scores_gemma":[0.1958987,0.0004052332,0.0006136445,0.0001395906,0.008268685,0.0001779379,0.0009743545,0.002735257,0.7907866],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.7535555,"threshold_uncertainty_score":0.9998252,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.030431415758318,"score_gpt":0.2335158871899702,"score_spread":0.2030844714316522,"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."}}