{"id":"W2409043072","doi":"10.1080/17583004.2016.1180586","title":"MAGGnet: An international network to foster mitigation of agricultural greenhouse gases","year":2016,"lang":"en","type":"article","venue":"Carbon Management","topic":"Water-Energy-Food Nexus Studies","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Agriculture and Agri-Food Canada; University of Manitoba","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Greenhouse gas; Agriculture; Environmental science; Natural resource economics; Greenhouse; Environmental economics; Environmental resource management; Business; Economics; Geography; Geology; Agronomy","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02622175,0.001399432,0.001134561,0.01327533,0.0009391968,0.004722464,0.004047841,0.002459239,0.02499465],"category_scores_gemma":[0.0305599,0.0005086969,0.001214325,0.01457673,0.0008295268,0.005609539,0.008424143,0.001789696,0.009685357],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003120217,"about_ca_system_score_gemma":0.01615626,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005196721,"about_ca_topic_score_gemma":0.004805414,"domain_scores_codex":[0.9942299,0.002621529,0.0005492866,0.0007968757,0.001399268,0.0004031987],"domain_scores_gemma":[0.9672125,0.01090711,0.004858187,0.006126951,0.006027919,0.004867277],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001393475,0.0002785924,0.007777101,0.005548316,0.0004315794,0.0003472936,0.001158434,0.006679401,0.01184647,0.05398196,0.4876353,0.4229222],"study_design_scores_gemma":[0.00009865889,0.00008034224,0.002702243,0.0008005341,0.0000896044,0.00006101277,0.0001169457,0.001249466,0.001174074,0.006440234,0.9871575,0.00002933649],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.03286652,0.06941379,0.1809345,0.05214405,0.007475152,0.006788661,0.3308282,0.04074904,0.2788001],"genre_scores_gemma":[0.07431351,0.02867852,0.4577892,0.007530103,0.001633691,0.01004316,0.3478482,0.007396132,0.06476743],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02622175,"threshold_uncertainty_score":0.1386755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01020509288767432,"score_gpt":0.2046263691116107,"score_spread":0.1944212762239363,"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."}}