{"id":"W4317892523","doi":"10.1515/ijfe-2021-0352","title":"Dietary modeling of greenhouse gases using OECD meat consumption/retail availability estimates","year":2023,"lang":"en","type":"article","venue":"International Journal of Food Engineering","topic":"Agriculture Sustainability and Environmental Impact","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Greenhouse gas; Consumption (sociology); Environmental science; Agricultural economics; Food waste; Quarter (Canadian coin); Carbon footprint; Econometrics; Economics; Waste management; Geography; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005876715,0.0004032977,0.0001714625,0.0006566725,0.0002329796,0.0007007038,0.0004360957,0.0003814077,0.001651172],"category_scores_gemma":[0.0010433,0.0002076325,0.0008553964,0.0008613893,0.0001944978,0.0003492123,0.0002286963,0.0002317257,0.000284554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001850521,"about_ca_system_score_gemma":0.0007535675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1556568,"about_ca_topic_score_gemma":0.07706793,"domain_scores_codex":[0.9998567,0.00005421024,0.000008621623,0.00004005978,0.00002011879,0.00002025223],"domain_scores_gemma":[0.9996363,0.0001776526,0.00005496974,0.00002431531,0.00009597156,0.00001089807],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003555615,0.00003434933,0.02330768,0.00002988787,0.00004528749,0.00004995383,0.00003094043,0.9709011,0.0003393458,0.001358771,0.0004332595,0.003433835],"study_design_scores_gemma":[0.000009814968,0.00003290104,0.01553533,0.0000110373,0.00003134846,0.00002462804,0.0000651898,0.9808368,0.0007015428,0.001109999,0.00162663,0.00001475871],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9381373,0.0002085763,0.03750307,0.0003025347,0.00002106305,0.00006624487,0.006332091,0.0003413572,0.01708781],"genre_scores_gemma":[0.9864082,0.000114435,0.009617934,0.00003650535,0.000004171911,0.00006007469,0.001530568,0.00002718229,0.002200934],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1556568,"threshold_uncertainty_score":0.3095015,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03570049624883534,"score_gpt":0.256199950334115,"score_spread":0.2204994540852796,"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."}}