{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004272874,0.0008258133,0.0009554574,0.002098887,0.000455179,0.001645573,0.001081823,0.00083243,0.001229492],"category_scores_gemma":[0.007426878,0.0002596507,0.001426667,0.001754983,0.0002874926,0.0007773708,0.0004451121,0.0008529706,0.0003064227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002316054,"about_ca_system_score_gemma":0.001320578,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07055692,"about_ca_topic_score_gemma":0.03100028,"domain_scores_codex":[0.9988402,0.00054357,0.00009262584,0.0001281663,0.0002611043,0.0001344099],"domain_scores_gemma":[0.9916434,0.006768768,0.0002570609,0.0002205857,0.001000867,0.0001092379],"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":[0.001024545,0.0003400911,0.02929344,0.0003906082,0.0003560346,0.0002042886,0.000179947,0.8575993,0.0006543022,0.003148234,0.002432732,0.1043765],"study_design_scores_gemma":[0.00001058531,0.0001832862,0.005954484,0.00002941017,0.00006778827,0.0000305576,0.0001147632,0.9921383,0.0003757181,0.0004880831,0.0005884191,0.00001862278],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9166281,0.009518428,0.05414442,0.0020113,0.0002227042,0.0001576807,0.0008693481,0.0009768206,0.01547123],"genre_scores_gemma":[0.984107,0.001733005,0.011905,0.00008876101,0.00002961366,0.0000385748,0.0004754998,0.00002663105,0.001595987],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07055692,"threshold_uncertainty_score":0.1402925,"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."}}