{"id":"W4386843073","doi":"10.18280/ria.370430","title":"A Comparative Analysis of Machine Learning Models for Crop Recommendation in India","year":2023,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Crop; Machine learning; Artificial intelligence; Agricultural engineering; Engineering; Geography; Forestry","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":[0.0003391823,0.00008656639,0.0002670636,0.00008545935,0.00009478769,0.00001962523,0.0001283735,0.00005367601,0.0002399795],"category_scores_gemma":[0.00004118287,0.00003772597,0.0001370408,0.002737585,0.00002321416,0.00009489451,0.00003416197,0.00008754912,0.00003392024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001473128,"about_ca_system_score_gemma":0.000002903493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001739595,"about_ca_topic_score_gemma":0.0007980264,"domain_scores_codex":[0.9991598,0.00005305302,0.0003120773,0.0002220548,0.0000672112,0.000185789],"domain_scores_gemma":[0.9993469,0.000400529,0.0001110983,0.00003625973,0.00007292403,0.00003229991],"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":[0.0001036713,0.0003123975,0.02694375,0.00003328981,0.0002481116,0.000002700989,0.006141358,0.7358825,0.135213,0.003596204,0.001022208,0.09050085],"study_design_scores_gemma":[0.0000225375,0.0001372826,0.0200759,0.00001847187,0.00005308461,3.234504e-7,0.002335,0.9526258,0.01891886,0.0007840808,0.004907373,0.0001213546],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9960241,0.00005765382,0.001290146,0.0005875605,0.00005967132,0.0002722272,0.00006222926,0.00005011269,0.001596353],"genre_scores_gemma":[0.9985858,0.0000652857,0.00006877575,0.00003135989,0.00003718035,0.00003636277,0.0006224817,5.169844e-7,0.0005522693],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2167433,"threshold_uncertainty_score":0.2627607,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1040433923070088,"score_gpt":0.3003989868468544,"score_spread":0.1963555945398456,"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."}}