{"id":"W4401122778","doi":"10.2139/ssrn.4910539","title":"Microclimate, Soil Moisture and Forage Yield Vary Spatially within a Temperate Tree-Based Intercropping System: From Competition to Facilitation","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Agroforestry and silvopastoral systems","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Government of Canada; Agriculture and Agri-Food Canada; Université du Québec en Outaouais","funders":"","keywords":"Intercropping; Microclimate; Temperate climate; Competition (biology); Forage; Environmental science; Facilitation; Yield (engineering); Tree (set theory); Agronomy; Agroforestry; Mathematics; Ecology; Biology","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":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001071204,0.0003811134,0.0004548538,0.00005142445,0.0003438984,0.0004841432,0.0003301387,0.0003725631,0.00002348126],"category_scores_gemma":[0.00003847882,0.0001706071,0.000192401,0.0001644355,0.00003765643,0.00009515096,0.0002463043,0.002499018,0.00005053638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000778598,"about_ca_system_score_gemma":0.0004053612,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.007411727,"about_ca_topic_score_gemma":0.06402963,"domain_scores_codex":[0.9972778,0.0002319615,0.000605567,0.0005712226,0.0002999376,0.001013486],"domain_scores_gemma":[0.9992363,0.0001173605,0.0002836599,0.0000798706,0.0001180665,0.0001647804],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001192101,0.0001925911,0.01148149,0.001277912,0.0008171076,0.000130648,0.002711892,0.005275,0.927367,0.01364309,0.0003020226,0.03560913],"study_design_scores_gemma":[0.005366591,0.0185711,0.137849,0.06894293,0.002779549,0.003377066,0.1356633,0.04021182,0.03481603,0.5347718,0.004470455,0.01318038],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9932725,0.001978485,0.001362988,0.001600608,0.0008126544,0.0004632289,0.0002458655,0.0001185798,0.0001450281],"genre_scores_gemma":[0.9980782,0.0001054413,0.00007729011,0.0001093329,0.0009918675,0.00003845893,0.0003132024,0.000005318414,0.0002808734],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.892551,"threshold_uncertainty_score":0.9998022,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01347230138022029,"score_gpt":0.2108251739294165,"score_spread":0.1973528725491962,"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."}}