{"id":"W4394731247","doi":"10.2139/ssrn.4792120","title":"Riparian Vegetation Influences Aquatic Greenhouse Gas Production in an Agricultural Landscape","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph; University of Waterloo","funders":"","keywords":"Riparian zone; Vegetation (pathology); Environmental science; Greenhouse gas; Agriculture; Production (economics); Agroforestry; Agricultural productivity; Geography; Ecology; Habitat; Archaeology; Economics","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":[0.0001503726,0.00009590595,0.0001717442,0.0003602291,0.0002108215,0.0006385366,0.0001691777,0.0001935382,0.002411149],"category_scores_gemma":[0.0006337041,0.0001181035,0.0001766427,0.0004740646,0.0003564442,0.0003371964,0.0003645207,0.0001256351,0.0002016572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003655285,"about_ca_system_score_gemma":0.0002337678,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01061519,"about_ca_topic_score_gemma":0.02291181,"domain_scores_codex":[0.9999039,0.00003821931,0.000003032885,0.00002486528,0.000008357723,0.00002165401],"domain_scores_gemma":[0.9996516,0.0001710966,0.00005488744,0.00001448061,0.0000244135,0.00008355403],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006174113,0.0001346306,0.9541245,0.00004049984,0.0001796516,0.0006495393,0.0004225913,0.004739648,0.02869957,0.0004549605,0.0002674234,0.009669601],"study_design_scores_gemma":[0.000006017941,0.00005103687,0.9960576,0.000001547628,0.00003279619,0.00005742045,0.0003251685,0.002757234,0.0003893349,0.000184998,0.0001333432,0.00000339291],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9993648,0.00001792266,0.00007271246,0.000008413908,6.231983e-7,7.54393e-7,0.00004178561,0.000002596056,0.0004904622],"genre_scores_gemma":[0.9997672,0.00001416856,0.00004660416,0.000003396802,7.844889e-7,5.886206e-7,0.00003704868,0.000003385334,0.0001267302],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01061519,"threshold_uncertainty_score":0.02110684,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008303023301175721,"score_gpt":0.2306314713796448,"score_spread":0.222328448078469,"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."}}