{"id":"W4385417197","doi":"10.21203/rs.3.rs-3193954/v1","title":"Internet of Things Sensors and Support Vector machine integrated intelligent irrigation system for agriculture industry","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Support vector machine; Naive Bayes classifier; Agriculture; Cluster analysis; Cloud computing; Irrigation; Population; Computer science; Internet of Things; Agricultural engineering; Machine learning; Artificial intelligence; Data mining; Engineering; Geography; World Wide Web","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":[0.0001612265,0.0002906022,0.0002279337,0.0003424707,0.0001779001,0.000464578,0.0003571456,0.0004275251,0.004264781],"category_scores_gemma":[0.0003027297,0.0001019784,0.0002731539,0.0005193811,0.00009318049,0.0006322414,0.0002080461,0.0002893545,0.001169637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000173617,"about_ca_system_score_gemma":0.0002182843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001137526,"about_ca_topic_score_gemma":0.001397863,"domain_scores_codex":[0.9998577,0.00001974138,0.00001253136,0.00002507778,0.000067851,0.00001715622],"domain_scores_gemma":[0.999881,0.00002075573,0.00001202427,0.00001248786,0.00006541613,0.000008190389],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007702323,0.0004904629,0.01256519,0.0006064595,0.000114345,0.0009449828,0.000148324,0.06768237,0.09808838,0.008043085,0.05368163,0.7568646],"study_design_scores_gemma":[0.00006122747,0.0005739037,0.0149261,0.00006265219,0.00006807956,0.0004656184,0.0002044779,0.8830509,0.05591159,0.006430343,0.03819938,0.00004566015],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2696301,0.002512967,0.6626779,0.003210061,0.001820768,0.0003849662,0.002624294,0.0116698,0.0454691],"genre_scores_gemma":[0.8955446,0.001117677,0.08232341,0.0002955763,0.0001481985,0.0001309973,0.002035339,0.00009034794,0.01831399],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004264781,"threshold_uncertainty_score":0.01426709,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08830925512846638,"score_gpt":0.326518896409759,"score_spread":0.2382096412812926,"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."}}