{"id":"W4319940996","doi":"10.15666/aeer/2101_439450","title":"SOIL MOISTURE IMPACT ON BIOMASS PARTITIONING AND RELATIVE CHLOROPHYLL CONTENT FOR LEGUME GRASS MIXTURES IN A CONTROLLED ENVIRONMENT","year":2023,"lang":"en","type":"article","venue":"Applied Ecology and Environmental Research","topic":"Soil Carbon and Nitrogen Dynamics","field":"Agricultural and Biological Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Agriculture and Agri-Food Canada; Ministry of Education of the People's Republic of China","keywords":"Agronomy; Water content; Biomass (ecology); Legume; Environmental science; Chlorophyll; Horticulture; Biology; Geology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002124427,0.0004191971,0.0003297252,0.0002754109,0.0003636346,0.0003144302,0.0003351232,0.0002178969,0.0006218478],"category_scores_gemma":[0.0001798015,0.0002272405,0.0002177709,0.0001741703,0.0003113184,0.000257089,0.000249849,0.0004801741,0.00009315251],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009165041,"about_ca_system_score_gemma":0.0004591586,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01016509,"about_ca_topic_score_gemma":0.01752332,"domain_scores_codex":[0.9998003,0.00003881076,0.00001419765,0.00006378651,0.00004030199,0.00004265671],"domain_scores_gemma":[0.9996578,0.00004417943,0.00007096575,0.00002481055,0.00003567238,0.0001665519],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.002706536,0.0002631263,0.001751631,0.00002948122,0.00002968709,0.00003338373,0.00005967707,0.000152423,0.9937748,0.00002115533,0.00002736889,0.001150643],"study_design_scores_gemma":[0.0005035098,0.02120597,0.3475986,0.00001588714,0.0002852828,0.0002258536,0.000470378,0.006692992,0.6200145,0.0001763596,0.002712475,0.00009830604],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9997028,0.00004757274,0.00008668454,0.000006690631,0.000003475658,0.000007107802,0.00008112261,0.000005742262,0.00005888859],"genre_scores_gemma":[0.9982363,0.00007371764,0.0005836489,0.00003850459,0.00000390513,0.00003480256,0.0004072984,0.00001227683,0.0006095244],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01016509,"threshold_uncertainty_score":0.02021188,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03633069312693511,"score_gpt":0.272936948691523,"score_spread":0.2366062555645879,"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."}}