{"id":"W2328751657","doi":"10.1021/es401426b","title":"Food Losses and Waste in China and Their Implication for Water and Land","year":2013,"lang":"en","type":"review","venue":"Environmental Science & Technology","topic":"Food Waste Reduction and Sustainability","field":"Agricultural and Biological Sciences","cited_by":239,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Food waste; Arable land; China; Food security; Agricultural economics; Environmental science; Hectare; Natural resource economics; Supply chain; Business; Food chain; Food supply; Environmental engineering; Environmental protection; Agricultural science; Waste management; Agriculture; Geography; Economics; Engineering; Ecology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002328664,0.000187043,0.000363751,0.00008259025,0.0002273763,0.00005420995,0.0001879635,0.0002021929,0.00001314934],"category_scores_gemma":[0.00001830675,0.00006093575,0.00003234606,0.0002384513,0.001315568,0.0001432485,0.000315578,0.0001307711,0.000001210892],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007202294,"about_ca_system_score_gemma":0.000006277625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001963189,"about_ca_topic_score_gemma":0.00002184571,"domain_scores_codex":[0.9988769,0.00002239209,0.0001914542,0.0005426448,0.00006241311,0.0003042174],"domain_scores_gemma":[0.9997581,0.00003446459,0.00006109737,0.00007674192,0.000003742037,0.00006584275],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000001305521,0.00002329455,0.000465584,0.0001304725,0.000002510699,1.128908e-7,0.00003505963,4.753663e-8,0.003769038,0.00007935224,0.000001466417,0.9954917],"study_design_scores_gemma":[0.0003369306,0.002285972,0.01073449,0.0003682048,0.00005018117,0.0003348937,0.008502111,0.00003364991,0.004328264,0.009425999,0.9627264,0.0008729022],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"review","genre_scores_codex":[0.6318265,0.3670255,5.576258e-7,0.0004493544,0.00001710465,0.0006321177,0.00002262734,0.00001610593,0.00001005976],"genre_scores_gemma":[0.4694899,0.5302952,0.00002166177,0.000004871869,0.00001635214,0.0001311875,0.00001644461,9.939635e-7,0.00002335798],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9946188,"threshold_uncertainty_score":0.4847265,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01237374512169062,"score_gpt":0.2285860981721498,"score_spread":0.2162123530504592,"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."}}