{"id":"W4236997875","doi":"10.21273/hortsci.45.5.771","title":"Greenhouse Cucumber Growth and Yield Response to Copper Application","year":2010,"lang":"en","type":"article","venue":"HortScience","topic":"Plant Physiology and Cultivation Studies","field":"Agricultural and Biological Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Ontario Ministry of Agriculture, Food and Rural Affairs; Ministry of Agriculture, Food and Rural Affairs; Ontario Centres of Excellence","keywords":"Cucumis; Dry weight; Horticulture; Copper; Nutrient; Greenhouse; Yield (engineering); Chemistry; Biology; Animal science; Agronomy","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002331338,0.0007699596,0.0006601391,0.0003403842,0.0004323524,0.0003309118,0.0004218889,0.0004333288,0.001827036],"category_scores_gemma":[0.0002733394,0.0003633711,0.0003095498,0.0002814705,0.0003238021,0.0002438489,0.000500749,0.001030392,0.0003814319],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001780875,"about_ca_system_score_gemma":0.0005491632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01641821,"about_ca_topic_score_gemma":0.0216819,"domain_scores_codex":[0.9997384,0.00003832832,0.0000216216,0.00008862267,0.00007189124,0.00004126383],"domain_scores_gemma":[0.9995453,0.00006538886,0.00009985598,0.00004204208,0.00009517508,0.0001522259],"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":[0.0002579956,0.00005087968,0.0003914506,0.00003525038,0.000006621602,0.00004558364,0.0000440421,0.00008807366,0.9983425,0.00001456477,0.00006648859,0.0006564833],"study_design_scores_gemma":[0.0001060189,0.003466111,0.04292526,0.00001749793,0.00007198245,0.0001934412,0.0001705399,0.002476948,0.9464227,0.00005927455,0.004027754,0.00006252334],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961182,0.0005945957,0.0008109074,0.0001087117,0.00005214705,0.00007727906,0.0008157272,0.0001918931,0.001230521],"genre_scores_gemma":[0.9855711,0.0004384873,0.002078073,0.0001729228,0.0000157482,0.0002426284,0.001786076,0.0001077387,0.009587236],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01641821,"threshold_uncertainty_score":0.03264529,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01450303679670879,"score_gpt":0.2226322853986361,"score_spread":0.2081292486019273,"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."}}