{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001520607,0.00009503472,0.0001096687,0.000005782339,0.0002243422,0.0000440126,0.00007406362,0.00007155367,9.4483e-7],"category_scores_gemma":[0.00000825958,0.00004005403,0.0000333205,0.0001413379,0.00001307629,0.0001161839,0.00003297119,0.00003721082,0.000008852639],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002857567,"about_ca_system_score_gemma":0.00000248476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000172874,"about_ca_topic_score_gemma":0.003459144,"domain_scores_codex":[0.9993546,0.00001029753,0.0001205089,0.0002383612,0.0000718609,0.0002043574],"domain_scores_gemma":[0.9997518,0.00009988583,0.00004716627,0.00002761097,0.00002681408,0.00004672613],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001556131,0.00007036238,0.005350746,0.00008031514,0.00004078958,0.000001705288,0.0005950709,0.001731264,0.2314454,0.008546774,0.001210647,0.7507713],"study_design_scores_gemma":[0.001381233,0.0008193961,0.325963,0.0001790552,0.0001121381,0.0001048367,0.005769974,0.3553451,0.003860081,0.05400084,0.2509679,0.001496556],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970734,0.0003803455,0.0003955868,0.001005669,0.0001094656,0.0002805422,0.0001444456,0.0001501894,0.0004603275],"genre_scores_gemma":[0.9985617,0.0004263574,0.00006971341,0.0001017024,0.0005133101,0.00005281358,0.0002517091,8.427023e-7,0.000021888],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7492748,"threshold_uncertainty_score":0.1930284,"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."}}