{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007877306,0.0001078389,0.00008964688,0.00002833944,0.00003346806,0.00001094236,0.0002368097,0.00001885991,0.000352571],"category_scores_gemma":[0.000004842606,0.00006841436,0.00003198724,0.0001077128,0.00005473967,0.0001619083,0.000435703,0.00001495531,0.00009858554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001210422,"about_ca_system_score_gemma":6.159306e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006722622,"about_ca_topic_score_gemma":0.0002489488,"domain_scores_codex":[0.999082,0.00002890343,0.0001622474,0.0002519056,0.0002785025,0.0001963878],"domain_scores_gemma":[0.9996545,0.00001186066,0.00005753627,0.0002022882,0.00001060476,0.00006317765],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0004045061,0.0009450938,0.2330092,0.000101834,0.001031151,0.0001204725,0.002487129,0.0274469,0.1085857,0.04382896,0.2060968,0.3759423],"study_design_scores_gemma":[0.0005605703,0.0002552555,0.9605748,0.0000858289,0.00004747115,0.000002318159,0.0001999713,0.00008329347,0.008420469,0.004981462,0.0244724,0.0003161394],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7347069,0.00001402459,0.0002226482,0.002129411,0.0003311213,0.0002488317,0.00001159681,0.00005662697,0.2622788],"genre_scores_gemma":[0.9928257,0.000006730489,0.0009974415,0.000397995,0.0001175143,0.00004130035,0.00000522591,0.000009554966,0.005598528],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7275656,"threshold_uncertainty_score":0.3860405,"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."}}