{"id":"W2562568621","doi":"10.1038/srep39307","title":"Yields and Nutritional of Greenhouse Tomato in Response to Different Soil Aeration Volume at two depths of Subsurface drip irrigation","year":2016,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Plant responses to water stress","field":"Agricultural and Biological Sciences","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Higher Education Discipline Innovation Project","keywords":"Aeration; Sugar; Irrigation; Greenhouse; Drip irrigation; Agronomy; Environmental science; Yield (engineering); Volume (thermodynamics); Chemistry; Horticulture; Animal science; Biology; Food science; Materials science","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":[0.0001427286,0.0003619186,0.0003763601,0.0001382015,0.0001106398,0.0002717494,0.0001383369,0.0002179004,0.0006149035],"category_scores_gemma":[0.0003008764,0.0001920465,0.0002303564,0.0001006986,0.0001728192,0.0003367364,0.0002677316,0.0005569408,0.00008377078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003005851,"about_ca_system_score_gemma":0.0001379841,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001081116,"about_ca_topic_score_gemma":0.001565725,"domain_scores_codex":[0.9998673,0.00002144551,0.00001081594,0.00004197693,0.00003660609,0.00002179783],"domain_scores_gemma":[0.9997157,0.0000612791,0.00008239449,0.00001906154,0.00004059103,0.00008089136],"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":[0.0001548264,0.00001318008,0.0004146326,0.00001561495,0.000004854564,0.00002152509,0.00002495147,0.00004904736,0.9989465,0.000004649297,0.000004693991,0.0003455303],"study_design_scores_gemma":[0.00003543751,0.003148165,0.08594049,0.000007182271,0.00006648343,0.0001209967,0.0002045371,0.001405836,0.9079032,0.0000691168,0.00106345,0.00003521788],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9992901,0.0001161587,0.0002682742,0.00001431176,0.000002977277,0.000007859296,0.00008254027,0.00001648337,0.0002011576],"genre_scores_gemma":[0.9978859,0.0001368057,0.0008423058,0.0000354475,0.000002700826,0.0000251192,0.0003176898,0.00001906406,0.0007350226],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001081116,"threshold_uncertainty_score":0.002180934,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01343244884570879,"score_gpt":0.2170402782427749,"score_spread":0.2036078293970661,"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."}}