{"id":"W4379468287","doi":"10.34133/plantphenomics.0059","title":"Global Wheat Head Detection Challenges: Winning Models and Application for Head Counting","year":2023,"lang":"en","type":"article","venue":"Plant Phenomics","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Agence Nationale de la Recherche","keywords":"Robustness (evolution); Computer science; Generalization; Data science; Competition (biology); Field (mathematics); Selection (genetic algorithm); Artificial intelligence; Head (geology); Machine learning; Data mining; Mathematics; Ecology","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":[0.01401476,0.003555385,0.003078866,0.001742875,0.001967749,0.003074104,0.005192392,0.005673549,0.00437902],"category_scores_gemma":[0.03284045,0.000763762,0.002710016,0.001327754,0.002021693,0.003783023,0.00515517,0.005180474,0.001286102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003214873,"about_ca_system_score_gemma":0.002697769,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01740319,"about_ca_topic_score_gemma":0.02343045,"domain_scores_codex":[0.9945966,0.002121873,0.0002302757,0.001658823,0.0008598728,0.0005325836],"domain_scores_gemma":[0.9828471,0.01150964,0.0006278243,0.001623015,0.002327905,0.001064508],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001034302,0.0006829524,0.01482501,0.0006083786,0.0004934667,0.0003170309,0.0003419259,0.6561248,0.003018476,0.02264974,0.06587853,0.2340254],"study_design_scores_gemma":[0.00007698969,0.0001152873,0.001055628,0.00003914626,0.00003078653,0.00005451011,0.000116065,0.9764858,0.0009806572,0.01792519,0.003087968,0.00003192515],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.251788,0.00490624,0.6973723,0.01186818,0.002212864,0.001279948,0.005694213,0.006329356,0.01854896],"genre_scores_gemma":[0.6494738,0.0004888022,0.3226088,0.00344611,0.0007461874,0.0007845172,0.009024443,0.001010301,0.01241702],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01740319,"threshold_uncertainty_score":0.07411808,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05293918415387757,"score_gpt":0.2419399342793215,"score_spread":0.1890007501254439,"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."}}