{"id":"W2533158144","doi":"10.11159/icesdp16.109","title":"Product Environmental Footprint for Feed Production in Thailand","year":2016,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Civil, Structural, and Environmental Engineering","topic":"Experience-Based Knowledge Management","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Footprint; Production (economics); Ecological footprint; Product (mathematics); Computer science; Sustainability; Geography; Mathematics; Economics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004244749,0.0003585113,0.0001133355,0.001447533,0.0003808803,0.001668434,0.0003012272,0.0002030164,0.01065547],"category_scores_gemma":[0.0006037146,0.00009583738,0.0005283466,0.00358973,0.0002258852,0.0006832636,0.0007796379,0.0002989843,0.0008215839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002323083,"about_ca_system_score_gemma":0.00137594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03349909,"about_ca_topic_score_gemma":0.03992053,"domain_scores_codex":[0.9994147,0.00007144918,0.00003220268,0.00006427467,0.000347768,0.00006961551],"domain_scores_gemma":[0.99906,0.0001402738,0.0002371372,0.00004349141,0.0004602118,0.00005896438],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008268845,0.0004096208,0.7669863,0.00276803,0.0003445539,0.003261638,0.001992324,0.02641087,0.01302978,0.003804777,0.01199439,0.1681709],"study_design_scores_gemma":[0.00001248046,0.000575438,0.9289873,0.0003373311,0.0001529472,0.0008319556,0.008771023,0.01035042,0.01417766,0.001435709,0.03428818,0.00007950748],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9070575,0.001073423,0.002630257,0.0002510587,0.00002812887,0.0001639142,0.03643673,0.0001305083,0.05222844],"genre_scores_gemma":[0.9832764,0.0006406562,0.001441382,0.00003803498,0.000007787502,0.00008570465,0.007851965,0.00002371758,0.006634374],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03349909,"threshold_uncertainty_score":0.06660819,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006011170416868639,"score_gpt":0.1854575961114562,"score_spread":0.1794464256945875,"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."}}