{"id":"W4221036482","doi":"10.18280/ria.360120","title":"IMLAPC: Interfused Machine Learning Approach for Prediction of Crops","year":2022,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Visvesvaraya Technological University","keywords":"Hyperparameter; Machine learning; Artificial intelligence; Decision tree; Perceptron; Multilayer perceptron; Computer science; Naive Bayes classifier; Classifier (UML); Agriculture; Algorithm; Artificial neural network; Support vector machine; Geography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001303946,0.0009513126,0.0008426468,0.001143416,0.0005282842,0.00131302,0.00149943,0.001398116,0.001762809],"category_scores_gemma":[0.003791106,0.0003037688,0.0007639446,0.0008839766,0.0003032468,0.001360469,0.0008507076,0.001578189,0.0005413159],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007582378,"about_ca_system_score_gemma":0.001187593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008957795,"about_ca_topic_score_gemma":0.00568653,"domain_scores_codex":[0.999295,0.0002115574,0.00005232737,0.0001919787,0.0001786425,0.00007040391],"domain_scores_gemma":[0.9986601,0.0007059405,0.00009707881,0.0001041234,0.0003931722,0.00003957376],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001823616,0.0002027307,0.005614839,0.0001072438,0.0001675928,0.0001249757,0.0000903654,0.5832739,0.002679954,0.002707778,0.003127578,0.4017207],"study_design_scores_gemma":[0.000002975597,0.00002635651,0.0002854931,0.000005709363,0.000006776534,0.00001625395,0.000007069298,0.9979189,0.0005466514,0.0007843952,0.0003952463,0.000004146681],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0462929,0.001038064,0.9461285,0.0003899714,0.0001375147,0.0001085253,0.0002752032,0.002891159,0.002738211],"genre_scores_gemma":[0.6764643,0.0004661336,0.3183387,0.0002235083,0.000101881,0.0003190658,0.0006303755,0.0001271631,0.003328895],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008957795,"threshold_uncertainty_score":0.0178113,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04231022883019413,"score_gpt":0.2250808309389122,"score_spread":0.1827706021087181,"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."}}