{"id":"W4399984784","doi":"10.3390/jimaging10070152","title":"Efficient Wheat Head Segmentation with Minimal Annotation: A Generative Approach","year":2024,"lang":"en","type":"article","venue":"Journal of Imaging","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Segmentation; Dice; Annotation; Deep learning; Context (archaeology); Generative model; Machine learning; Generative grammar; Pattern recognition (psychology); Image segmentation","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000160514,0.00007999894,0.00009939485,0.00001814164,0.00008820381,0.0001648522,0.00006501751,0.00001453881,0.00003853336],"category_scores_gemma":[0.00000511139,0.00002179505,0.00006384504,0.0002666092,0.00002192919,0.0001455449,0.00000955134,0.0001090793,0.000005404581],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000307847,"about_ca_system_score_gemma":0.00001264793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000842472,"about_ca_topic_score_gemma":0.000008629431,"domain_scores_codex":[0.999351,0.00003357481,0.0001751782,0.0001062878,0.0002233336,0.0001106339],"domain_scores_gemma":[0.9997021,0.00004892231,0.00007374351,0.00001215218,0.0001126294,0.00005047604],"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":[0.00009020947,0.0001678298,0.00675201,0.00002564219,0.00007663971,0.0001556096,0.002268325,0.002261934,0.8572326,0.0001713923,0.009808011,0.1209898],"study_design_scores_gemma":[0.003335291,0.004715478,0.5099328,0.002191953,0.0007628124,0.01152217,0.05822553,0.1458489,0.1543586,0.0006081793,0.1062069,0.002291394],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9927347,0.001323314,0.0009694072,0.003734282,0.0001737203,0.00007020302,0.000003621582,0.00001801916,0.000972739],"genre_scores_gemma":[0.9955938,0.000008807689,0.003198583,0.0002061289,0.0008741017,0.00000244794,0.000009371213,6.693918e-7,0.0001060194],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7028741,"threshold_uncertainty_score":0.1589673,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01399958247406314,"score_gpt":0.2352962832839071,"score_spread":0.221296700809844,"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."}}