{"id":"W4321385045","doi":"10.1109/cistem55808.2022.10043876","title":"Kinematic Navigation Control of Differential Drive Agricultural Robot","year":2022,"lang":"en","type":"article","venue":"","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Real Time Kinematic; Robot; Skid (aerodynamics); Agriculture; Kinematics; Footprint; Mobile robot; Agricultural machinery; Sustainability; Agricultural engineering; Greenhouse; Population; Robot kinematics; Sustainable agriculture; Engineering; Computer science; Control engineering; Artificial intelligence; Geography; GNSS applications","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00006762969,0.0001044002,0.0001802014,0.000005473241,0.0002303537,0.00001912616,0.0001939307,0.00002979589,0.003226975],"category_scores_gemma":[0.000007877285,0.00002856999,0.0001287865,0.0002727229,0.00002236033,0.00006874436,0.00007456062,0.0001023077,0.00001401045],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001771534,"about_ca_system_score_gemma":0.000002349653,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001103326,"about_ca_topic_score_gemma":0.00005183066,"domain_scores_codex":[0.9990842,0.00008310442,0.0002183602,0.0001639231,0.0002904067,0.0001600398],"domain_scores_gemma":[0.9996567,0.00009392633,0.0001159608,0.00003041758,0.00005555685,0.00004739158],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00001679625,0.0001450487,0.003076574,0.000005120679,0.00002312524,0.000001416648,0.00009022839,0.0001494793,0.9901918,0.001183445,0.002350701,0.002766334],"study_design_scores_gemma":[0.000658575,0.0007061015,0.9729804,0.00001520746,0.0000677585,0.00003493912,0.002877685,0.0003398147,0.01740039,0.0004277891,0.004155488,0.0003357854],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968807,0.00003252463,0.00004058303,0.001208955,0.0001814091,0.0002332401,0.00004163929,0.00005137732,0.001329553],"genre_scores_gemma":[0.9986307,0.000001204009,0.00003179535,0.0001299912,0.0002009772,0.00004541474,0.0002484617,3.500957e-7,0.0007111171],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9727913,"threshold_uncertainty_score":0.9976842,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007240311307222528,"score_gpt":0.1813021045262685,"score_spread":0.174061793219046,"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."}}