{"id":"W1428583425","doi":"10.1016/j.cropro.2015.08.019","title":"Structural equation modeling reveals complex relationships in mixed forage swards","year":2015,"lang":"en","type":"article","venue":"Crop Protection","topic":"Ecology and Vegetation Dynamics Studies","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"Alberta Ministry of Agriculture and Forestry; University of Alberta; Saskatchewan Ministry of Agriculture","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta","keywords":"Biology; Agronomy; Forb; Weed; Cirsium arvense; Perennial plant; Forage; Interspecific competition; Thistle; Legume; Biomass (ecology); Tussock; Competition (biology); Deserts and xeric shrublands; Vegetation (pathology); Noxious weed; Ecology; Grassland; Habitat","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005157354,0.00007111358,0.00008119418,0.00004852442,0.0002486632,0.0000151116,0.00005248516,0.00007778117,0.00005644115],"category_scores_gemma":[0.0002748016,0.00007002937,0.00001799904,0.0002043724,0.00005529458,0.0002659159,0.00004939826,0.0001774356,0.0001318617],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003082356,"about_ca_system_score_gemma":0.000008104315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002944612,"about_ca_topic_score_gemma":0.001809966,"domain_scores_codex":[0.9992386,0.0001478676,0.0001916062,0.0001686539,0.0001247681,0.0001284971],"domain_scores_gemma":[0.9997727,0.0000163701,0.00006815816,0.00008641195,0.00002142087,0.00003488311],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003724639,0.0000167757,0.1263686,0.000007947378,0.000004949837,0.000001114331,0.001750095,0.8671645,0.002044609,0.0007925706,0.00008426209,0.001727335],"study_design_scores_gemma":[0.00025367,0.00003140259,0.1944774,0.000004318266,0.000003113136,0.00000271834,0.0002292856,0.7757071,0.00003143511,0.02916216,0.00002660791,0.00007080462],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9032764,0.000009323619,0.09375042,0.0003048292,0.0001661183,0.0003560765,0.000001015499,0.00004022216,0.002095577],"genre_scores_gemma":[0.9975076,0.000001202096,0.00209462,0.00002506916,0.00002212487,0.00007435522,0.00001191614,0.000006042425,0.0002570293],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09423122,"threshold_uncertainty_score":0.2855715,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1519523451767038,"score_gpt":0.2878200850925497,"score_spread":0.1358677399158459,"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."}}