{"id":"W4385803554","doi":"10.1101/2023.08.13.553080","title":"NanoPyx: super-fast bioimage analysis powered by adaptive machine learning","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Fluorescence Microscopy Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Innovation Cluster (Canada)","funders":"Fundação para a Ciência e a Tecnologia; HORIZON EUROPE Framework Programme; European Commission; Chan Zuckerberg Initiative; European Molecular Biology Organization","keywords":"Computer science; Python (programming language); Plug-in; Implementation; Computer engineering; Coding (social sciences); Image processing; Artificial intelligence; Distributed computing; Image (mathematics); Programming language","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":[0.0009066188,0.0007972292,0.0006933596,0.0005414591,0.0004408346,0.001091591,0.002581669,0.0008955688,0.007366112],"category_scores_gemma":[0.001943986,0.0004785266,0.0006113016,0.0005051707,0.0007162805,0.001679852,0.002149627,0.002168861,0.002397553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000852827,"about_ca_system_score_gemma":0.001426926,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003659158,"about_ca_topic_score_gemma":0.004372219,"domain_scores_codex":[0.9996288,0.00004620823,0.00001493071,0.00009664849,0.0001721149,0.00004135472],"domain_scores_gemma":[0.9995742,0.000163865,0.00003440361,0.0000989843,0.00007829064,0.00005016711],"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":[0.001076671,0.0002813245,0.00390702,0.0008357896,0.000498627,0.0004914412,0.0004221433,0.2668584,0.1533383,0.06152928,0.1013837,0.4093773],"study_design_scores_gemma":[0.00005443317,0.00003739471,0.0004399712,0.00002058967,0.000014671,0.00008425565,0.00001432502,0.9503605,0.02278572,0.01322034,0.01293137,0.00003644192],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008369944,0.0002904727,0.9486675,0.0003415984,0.0001068719,0.00007231204,0.0005323566,0.03914603,0.002472968],"genre_scores_gemma":[0.1533264,0.0004319684,0.8317941,0.0005554809,0.00008359985,0.0004119818,0.002141809,0.007184833,0.004069757],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007366112,"threshold_uncertainty_score":0.02464211,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009916765968233206,"score_gpt":0.2387412384080249,"score_spread":0.2288244724397917,"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."}}