{"id":"W3125050108","doi":"10.26685/urncst.192","title":"Bioremediation: How to Decrease Greenhouse Gas Emissions Through Cattle","year":2020,"lang":"en","type":"article","venue":"Undergraduate Research in Natural and Clinical Science and Technology (URNCST) Journal","topic":"Ruminant Nutrition and Digestive Physiology","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Greenhouse gas; Carbon footprint; Agriculture; Rumen; Population; Environmental science; Beef cattle; Livestock; Bioremediation; Biotechnology; Methane; Biology; Environmental protection; Ecology; Animal science; Food science; Contamination; Environmental health","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0008708776,0.0003942772,0.000461256,0.0003628411,0.0004375635,0.0007791363,0.0008481175,0.0007992846,0.004086041],"category_scores_gemma":[0.0005886663,0.0001203391,0.0004749208,0.0003861028,0.0004051346,0.0007941641,0.0002976705,0.0005478791,0.0007426533],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000704012,"about_ca_system_score_gemma":0.0007257723,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004149943,"about_ca_topic_score_gemma":0.004823213,"domain_scores_codex":[0.9996886,0.00009831531,0.00001055241,0.00005433906,0.00008857686,0.00005953811],"domain_scores_gemma":[0.9998078,0.00003675015,0.00005398003,0.00001853685,0.00004684172,0.00003608529],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001668928,0.001910921,0.007783817,0.003083311,0.0001440852,0.0001380892,0.0003880053,0.001569364,0.5974808,0.001717251,0.003240893,0.3808745],"study_design_scores_gemma":[0.0005347854,0.0228938,0.06726514,0.002230835,0.00112825,0.001134765,0.00273492,0.006986277,0.6711587,0.005879726,0.217889,0.000163779],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8644962,0.05169068,0.04727342,0.01545398,0.001066587,0.0007140395,0.0009879123,0.0005624308,0.01775467],"genre_scores_gemma":[0.8759155,0.03894049,0.06422389,0.003451423,0.0002752041,0.000589957,0.0006848403,0.00006159396,0.01585699],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004149943,"threshold_uncertainty_score":0.01366919,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1006263707077225,"score_gpt":0.380181286811058,"score_spread":0.2795549161033354,"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."}}