{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003588046,0.0001075787,0.0001677073,0.00001711886,0.0004067063,0.00002100747,0.0002712718,0.00004030378,0.0007310077],"category_scores_gemma":[0.00005393289,0.00004742439,0.0001477166,0.0004109013,0.00003731788,0.00005980601,0.0001150758,0.0001852544,0.000008918719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002545304,"about_ca_system_score_gemma":0.00000430098,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008715616,"about_ca_topic_score_gemma":0.00001968163,"domain_scores_codex":[0.9989808,0.0000729985,0.0003132701,0.0002799424,0.0001453684,0.0002076298],"domain_scores_gemma":[0.9995465,0.0001431182,0.0001335122,0.00006092403,0.00007064912,0.00004534655],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000180115,0.0007463109,0.01183516,0.00006516969,0.00004049803,0.000001828943,0.001473655,0.06759227,0.8257124,0.00148074,0.003232843,0.087639],"study_design_scores_gemma":[0.000138678,0.002661166,0.003562264,0.00003117574,0.00005385905,0.00004912305,0.009551759,0.4753208,0.2307573,0.0006778913,0.276753,0.0004429921],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9820194,0.0006088031,0.01085282,0.0007676441,0.0004487607,0.0008471876,0.0004250386,0.0001552869,0.003875036],"genre_scores_gemma":[0.9963173,0.00002029206,0.000326546,0.00005253435,0.0001823237,0.0001201477,0.0004496947,0.000001529971,0.002529678],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5949551,"threshold_uncertainty_score":0.8004021,"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."}}