{"id":"W4401567822","doi":"10.1109/icjece.2024.3400048","title":"A Novel Ensemble Machine Learning Algorithm for Predicting the Suitable Crop to Cultivate Based on Soil and Environment Characteristics","year":2024,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Ensemble learning; Computer science; Artificial intelligence; Machine learning; Algorithm","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001416078,0.00009236384,0.0001121026,0.00002866563,0.0001435138,0.0001544559,0.00006936264,0.00003574747,0.000004552077],"category_scores_gemma":[0.00002069873,0.00003337459,0.00004171563,0.0001263994,0.00000718146,0.00003752294,0.00001013543,0.0002049745,5.577594e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002975156,"about_ca_system_score_gemma":0.000014878,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001928755,"about_ca_topic_score_gemma":0.0001085442,"domain_scores_codex":[0.9994814,0.000008718515,0.0001164334,0.0001095551,0.00006890952,0.000214929],"domain_scores_gemma":[0.9994504,0.0002550319,0.00002275073,0.0000131156,0.00001955037,0.0002391369],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001312885,0.00002394082,0.001666342,0.00002579097,0.00005975069,0.00005452414,0.0002317673,0.01166372,0.04937232,0.0001391845,0.001372284,0.9353772],"study_design_scores_gemma":[0.0000943671,0.0006377863,0.01593485,0.0000745068,0.00001762526,0.00007315352,0.000008022754,0.8970173,0.000402077,0.000005266038,0.0856205,0.0001145224],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8293486,0.001279075,0.1644184,0.004392471,0.0002890507,0.0001916928,0.00004376027,0.00002545563,0.00001153517],"genre_scores_gemma":[0.996919,0.00002181333,0.00184375,0.0003232106,0.0008119454,0.000004472037,0.000007110675,0.000002077127,0.00006667169],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9352627,"threshold_uncertainty_score":0.1489422,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006400467257085894,"score_gpt":0.1573745853313619,"score_spread":0.150974118074276,"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."}}