{"id":"W4405175183","doi":"10.1016/j.compag.2024.109789","title":"A novel method for detecting missing seedlings based on UAV images and rice transplanter operation information","year":2024,"lang":"en","type":"article","venue":"Computers and Electronics in Agriculture","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer vision; Artificial intelligence; Agricultural engineering; Computer science; Engineering; Remote sensing; 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":[0.0002327452,0.000158202,0.0001390795,0.0000254918,0.00019168,0.0003932801,0.00006973649,0.0001181288,0.000001849301],"category_scores_gemma":[0.00001111572,0.00005622349,0.00005142396,0.0002341661,0.00001060305,0.0002973416,0.00001393632,0.0002076496,7.293551e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003370546,"about_ca_system_score_gemma":0.000007671117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004334044,"about_ca_topic_score_gemma":0.0001128855,"domain_scores_codex":[0.9992355,0.00002406866,0.0001686187,0.0002432501,0.00009288728,0.0002357441],"domain_scores_gemma":[0.9995722,0.0002908361,0.00003310561,0.00002049266,0.00003518978,0.00004823829],"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":[0.00005309101,0.00004533927,0.0001292445,0.0001474673,0.00002009215,0.00000216449,0.0006769081,0.000827155,0.7631027,0.0006498045,0.00263407,0.231712],"study_design_scores_gemma":[0.002058869,0.002162953,0.06416509,0.001475537,0.0001579155,0.000350509,0.0009147869,0.6740109,0.0546551,0.00120011,0.197259,0.001589235],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6696098,0.005848104,0.3021993,0.01903179,0.000681891,0.001566823,0.000145131,0.0003753547,0.0005418076],"genre_scores_gemma":[0.9794143,0.0002266004,0.01766992,0.001843574,0.0004647867,0.00004454308,0.0002850308,0.000002096414,0.00004913793],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7084476,"threshold_uncertainty_score":0.3792409,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007702217302529875,"score_gpt":0.2219384445698341,"score_spread":0.2142362272673043,"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."}}