{"id":"W3043292641","doi":"10.1007/978-3-030-65414-6_27","title":"AutoCount: Unsupervised Segmentation and Counting of Organs in Field Images","year":2020,"lang":"en","type":"preprint","venue":"Lecture notes in computer science","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Benchmark (surveying); Artificial intelligence; Computer science; Segmentation; Pattern recognition (psychology); Annotation; Deep learning; Field (mathematics); Convolutional neural network; Task (project management); Range (aeronautics); Computer vision; Mathematics; Cartography","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":[0.00152703,0.00263768,0.00293141,0.006981918,0.001283567,0.002982341,0.005445883,0.002414183,0.008906058],"category_scores_gemma":[0.002966907,0.002257871,0.001987523,0.004702519,0.00111186,0.002924313,0.00348014,0.001226992,0.005508446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001167662,"about_ca_system_score_gemma":0.002154284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007494923,"about_ca_topic_score_gemma":0.01724215,"domain_scores_codex":[0.9983332,0.0001709371,0.00009193426,0.0005898082,0.0005892389,0.0002250529],"domain_scores_gemma":[0.9977477,0.0007477803,0.0001930686,0.0006799166,0.0004842639,0.0001473396],"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":[0.000852501,0.0002582382,0.004429011,0.0007690267,0.0004052854,0.0002728377,0.0002137167,0.02121018,0.1007671,0.004265165,0.04440038,0.8221565],"study_design_scores_gemma":[0.0001727734,0.0001635861,0.007914932,0.00006700434,0.0001269987,0.0008829754,0.0001578925,0.8739982,0.08600546,0.01406011,0.01634641,0.0001036613],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03216682,0.0006406175,0.8953989,0.0001587928,0.0001572514,0.000295016,0.004118249,0.06517877,0.001885637],"genre_scores_gemma":[0.06504937,0.0002751494,0.916777,0.0001294617,0.0001190966,0.0003336289,0.008396948,0.004266406,0.004652957],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008906058,"threshold_uncertainty_score":0.02979374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0152809565830624,"score_gpt":0.2329811601630468,"score_spread":0.2177002035799844,"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."}}