{"id":"W4411491035","doi":"10.4018/979-8-3373-5182-7.ch007","title":"Reducing Food Wastage Through Accurate Demand Prediction Using Generative AI","year":2025,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Food Waste Reduction and Sustainability","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"SAIT Polytechnic","funders":"","keywords":"Inefficiency; Food waste; Natural resource economics; Population; Business; Greenhouse gas; Food spoilage; Environmental science; Economics; Ecology; Waste management; Engineering; Market economy; Biology","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":[],"consensus_categories":[],"category_scores_codex":[0.0001502915,0.0004220759,0.0004327177,0.00001470091,0.000435483,0.0001569712,0.0002277666,0.0005051803,0.00008023295],"category_scores_gemma":[0.00003778151,0.0001978007,0.000279834,0.00006249476,0.0001465062,0.0001382053,0.0001748379,0.0003614233,0.000005555299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000462419,"about_ca_system_score_gemma":0.0001360484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002260356,"about_ca_topic_score_gemma":0.0002190714,"domain_scores_codex":[0.9980746,0.00007501579,0.0004611053,0.0007125718,0.0003052613,0.0003714331],"domain_scores_gemma":[0.9992137,0.0000386971,0.0002161914,0.0001497365,0.0002626284,0.0001190313],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001713967,0.00005136582,0.00004006084,0.0001248994,0.0003009463,0.00002350579,0.0002328473,0.0003545786,0.003632586,0.9340235,0.005249565,0.05579477],"study_design_scores_gemma":[0.0009243503,0.002224404,0.0004043645,0.00103935,0.0005266527,0.000165047,0.004083228,0.001476675,0.003838985,0.8183584,0.1648169,0.002141697],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.07118773,0.0006448737,0.0002151537,0.0006557257,0.001183588,0.0009614307,0.00115463,0.0002514671,0.9237454],"genre_scores_gemma":[0.9063299,0.00002615801,0.0003686807,0.0008304104,0.00142222,0.00002263631,0.00007458842,0.000005093653,0.09092034],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.8351421,"threshold_uncertainty_score":0.8066078,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03475417798056574,"score_gpt":0.2653912655085623,"score_spread":0.2306370875279966,"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."}}