{"id":"W2536852724","doi":"10.1016/j.ifacol.2016.10.066","title":"Feature-based visual tracking for agricultural implements","year":2016,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Scale-invariant feature transform; GNSS applications; Artificial intelligence; Computer science; Computer vision; Feature (linguistics); Histogram of oriented gradients; Outlier; Feature tracking; Pixel; Histogram; Pattern recognition (psychology); Feature extraction; Image (mathematics); Global Positioning System","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":[0.0004114574,0.0003265058,0.0004359519,0.0008097116,0.0002383164,0.0007714138,0.0007619289,0.0004779422,0.003958283],"category_scores_gemma":[0.0009046355,0.0002082046,0.0003467289,0.0009920251,0.0001768799,0.000631895,0.0003871255,0.0002446677,0.002191501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005216544,"about_ca_system_score_gemma":0.0005321925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007339074,"about_ca_topic_score_gemma":0.01087446,"domain_scores_codex":[0.9997012,0.00002420537,0.00000913328,0.0000873547,0.0001446349,0.00003339776],"domain_scores_gemma":[0.9996386,0.00003760223,0.00004347889,0.00009898162,0.0001682357,0.00001305223],"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":[0.0002128406,0.00007659895,0.004550262,0.0002020769,0.00005217719,0.0000760634,0.00006660022,0.02464467,0.1609157,0.001030453,0.005341175,0.8028315],"study_design_scores_gemma":[0.00004980613,0.0004281545,0.0629159,0.0001192862,0.00006692553,0.0005270772,0.0001185758,0.7107683,0.1802025,0.002306528,0.04240738,0.00008950965],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1038078,0.0007526167,0.8755677,0.00008037165,0.0001273377,0.0001084643,0.001111757,0.009050654,0.009393387],"genre_scores_gemma":[0.6769279,0.000473822,0.3088384,0.0000665144,0.0000318104,0.0001008423,0.002426475,0.0002835033,0.01085077],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007339074,"threshold_uncertainty_score":0.01459271,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02131971084508913,"score_gpt":0.2602395333004721,"score_spread":0.238919822455383,"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."}}