{"id":"W4392091108","doi":"10.3390/act13030081","title":"Real-Time Point Recognition for Seedlings Using Kernel Density Estimators and Pyramid Histogram of Oriented Gradients","year":2024,"lang":"en","type":"article","venue":"Actuators","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Histogram; Pyramid (geometry); Estimator; Kernel (algebra); Artificial intelligence; Point (geometry); Pattern recognition (psychology); Kernel density estimation; Mathematics; Computer science; Statistics; Image (mathematics); Geometry; Pure mathematics","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":[0.0001526568,0.0001308086,0.0001750534,0.00002024152,0.0001202554,0.00004054448,0.00006081033,0.00009065016,0.00004021],"category_scores_gemma":[0.00006235924,0.00005062327,0.00009939817,0.0002732714,0.00006333685,0.0001459762,0.00003346995,0.00005917851,0.00001003168],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003091101,"about_ca_system_score_gemma":0.000007780262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005736337,"about_ca_topic_score_gemma":0.00004756968,"domain_scores_codex":[0.9992172,0.00001768793,0.0001899584,0.0002686315,0.0001176414,0.0001888443],"domain_scores_gemma":[0.9995731,0.000161883,0.00006675637,0.00003149207,0.00008130402,0.00008551115],"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":[0.0001132314,0.0002033349,0.02692258,0.000152314,0.0001385469,0.00001313957,0.001159604,0.000002102409,0.7727294,0.0004687435,0.004103345,0.1939936],"study_design_scores_gemma":[0.001993734,0.003633617,0.595589,0.002174493,0.001562736,0.0002692104,0.003929204,0.02425249,0.2732216,0.03289902,0.056924,0.003550898],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9990382,0.00004474102,0.00005519226,0.0001567132,0.0002337093,0.0002405603,0.00005034969,0.0001063299,0.00007425692],"genre_scores_gemma":[0.9983134,0.0000313767,0.001248705,0.00003709546,0.0001465087,0.000007898423,0.0001399364,0.000001840732,0.00007328532],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5686665,"threshold_uncertainty_score":0.2064357,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0170266985161683,"score_gpt":0.2301064413548357,"score_spread":0.2130797428386673,"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."}}