{"id":"W4387872546","doi":"10.1109/icc45041.2023.10279747","title":"CROP: Cluster-Based Routing Using Optimized Framework for IoT-Based Precision Agriculture","year":2023,"lang":"en","type":"article","venue":"","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Ministry of Electronics and Information technology","keywords":"Computer science; Scalability; Agriculture; Precision agriculture; Routing (electronic design automation); Crop; Field (mathematics); Cluster (spacecraft); Agricultural engineering; Distributed computing; Computer network; Database; Engineering; Mathematics; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003973272,0.0002769501,0.0003083646,0.00002357016,0.0005862932,0.0001910063,0.000323526,0.000340332,0.0002779639],"category_scores_gemma":[0.0002534392,0.0000819591,0.0003342353,0.001164099,0.00003217121,0.00008092858,0.00006774432,0.0001834913,0.00006698779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003999822,"about_ca_system_score_gemma":0.00002009766,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000864442,"about_ca_topic_score_gemma":0.0001277639,"domain_scores_codex":[0.9981796,0.00008316042,0.0003368588,0.0005162006,0.000320028,0.0005642097],"domain_scores_gemma":[0.9981605,0.001295402,0.0001424176,0.00008901252,0.0001647859,0.000147832],"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":[0.0007593622,0.0005370409,0.004489133,0.00007738129,0.00008706778,0.00001773592,0.0001859948,0.09829462,0.7690204,0.002497196,0.08471352,0.03932059],"study_design_scores_gemma":[0.005683824,0.001851109,0.06203652,0.001198188,0.0003241449,0.00001513367,0.00256962,0.5899091,0.1785825,0.007266555,0.1469858,0.003577556],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.972442,0.00004672385,0.01772333,0.006768078,0.0005716327,0.001122644,0.00007923302,0.0008120108,0.0004343883],"genre_scores_gemma":[0.9004257,0.000003817268,0.09278011,0.002918347,0.001784085,0.0001229048,0.0006336526,0.000005536116,0.001325832],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5904379,"threshold_uncertainty_score":0.4509352,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04155665841686834,"score_gpt":0.2756775239790728,"score_spread":0.2341208655622045,"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."}}