{"id":"W4378364711","doi":"10.1109/icsmdi57622.2023.00094","title":"Agrobot: Agricultural Robot using IoT and Machine Learning (ML)","year":2023,"lang":"en","type":"article","venue":"","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Agriculture; Natural disaster; Livelihood; Agrarian society; Business; Agricultural productivity; Production (economics); Food security; Natural resource; Natural resource economics; Economics; Geography; Political science","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":[0.0002431257,0.0006869442,0.0004522079,0.0004904068,0.0002812074,0.0005230568,0.00104549,0.0007058373,0.01372078],"category_scores_gemma":[0.0003135611,0.0002878821,0.0003381101,0.0002620688,0.0002770247,0.0007622369,0.0008034525,0.0004730862,0.003929864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000225629,"about_ca_system_score_gemma":0.0003609706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001238797,"about_ca_topic_score_gemma":0.001427198,"domain_scores_codex":[0.9998056,0.00001642011,0.00001264057,0.00006822022,0.00007058467,0.00002660366],"domain_scores_gemma":[0.9998919,0.00002746442,0.00001555635,0.00001841756,0.00002767647,0.00001895203],"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":[0.001166678,0.0008211342,0.006108976,0.0009809501,0.0001643493,0.001058214,0.0003017238,0.02868961,0.1351133,0.005846119,0.05069965,0.7690492],"study_design_scores_gemma":[0.0004106672,0.001609466,0.01066048,0.0002685016,0.0001476416,0.001389766,0.0001986487,0.7638101,0.07383578,0.009799418,0.1376783,0.0001912057],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0453007,0.001219525,0.8292821,0.0006081125,0.0006372044,0.0007471709,0.001282903,0.08104061,0.0398817],"genre_scores_gemma":[0.6299589,0.0008139446,0.3146057,0.001645286,0.0001488581,0.001070122,0.001865337,0.0008985464,0.0489934],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01372078,"threshold_uncertainty_score":0.04590058,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04503041925274056,"score_gpt":0.2455775364256073,"score_spread":0.2005471171728667,"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."}}