{"id":"W4400422555","doi":"10.36548/jismac.2024.3.002","title":"Smart-Agro: Enhancing Crop Management with Agribot","year":2024,"lang":"en","type":"article","venue":"Journal of ISMAC","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Crop management; Crop; Business; Agricultural engineering; Agroforestry; Environmental science; Agronomy; Engineering; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002678942,0.0005033233,0.0002323722,0.0002749122,0.0002171351,0.0005685388,0.0008583226,0.0003975609,0.007052467],"category_scores_gemma":[0.0003593561,0.000223005,0.0002443749,0.0002507164,0.0002058715,0.0006036838,0.0008380217,0.0003675619,0.002587407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004778693,"about_ca_system_score_gemma":0.0005340559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002831,"about_ca_topic_score_gemma":0.005324364,"domain_scores_codex":[0.9998098,0.00001767497,0.000006638135,0.00004555639,0.00008872931,0.00003161482],"domain_scores_gemma":[0.9998667,0.00002741461,0.00001757757,0.00002365032,0.00003712817,0.00002762894],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000885185,0.000439072,0.006240353,0.0009292473,0.00008503523,0.0007519402,0.0004003584,0.03372228,0.3866459,0.006661809,0.05341677,0.5098221],"study_design_scores_gemma":[0.0001768741,0.0007813984,0.01618058,0.0001307879,0.0001036207,0.0007979412,0.0003200304,0.4429418,0.1722988,0.006192014,0.3599062,0.0001700409],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1771249,0.001596853,0.6314188,0.0009839808,0.0004160497,0.0007367898,0.004540164,0.1103045,0.07287794],"genre_scores_gemma":[0.54144,0.0007733245,0.4262937,0.0007540253,0.0000402054,0.0002882604,0.003703881,0.002238792,0.02446779],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007052467,"threshold_uncertainty_score":0.02359289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006953897746058131,"score_gpt":0.1953035843718781,"score_spread":0.18834968662582,"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."}}