{"id":"W2288603533","doi":"10.1093/bioinformatics/btv746","title":"Robust quantitative scratch assay","year":2015,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute; Princess Margaret Cancer Centre; University Health Network; University of Toronto","funders":"","keywords":"Artificial intelligence; Outlier; Computer science; Machine learning; Mathematics","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":[0.0004425571,0.0001172324,0.0001206547,0.00005463679,0.00003525769,0.00004650443,0.0001880668,0.0001014814,0.00001122474],"category_scores_gemma":[0.0002945777,0.0001045362,0.000071103,0.0001269507,0.00006275031,0.00001221602,0.0001188516,0.00006081085,0.0001263435],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001829547,"about_ca_system_score_gemma":0.00008488686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001429226,"about_ca_topic_score_gemma":0.00002103456,"domain_scores_codex":[0.9992778,0.00003178309,0.0002473266,0.0001102576,0.0001655125,0.0001673224],"domain_scores_gemma":[0.9992055,0.000008941902,0.0001051277,0.0003652383,0.0002088053,0.0001064376],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007659742,0.0001365133,0.002144004,0.00005241024,0.0001951465,0.000006860368,0.0009312709,0.0005326641,0.04056738,0.0009781545,0.9461968,0.008182233],"study_design_scores_gemma":[0.001266379,0.001145526,0.0002555965,0.00003622405,0.0001270346,0.00003046621,0.004374713,0.05760578,0.521503,0.000941982,0.4117016,0.001011719],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08636662,0.0007372047,0.7094873,0.0003615485,0.0001302528,0.0004481512,0.00001863585,0.0001830707,0.2022672],"genre_scores_gemma":[0.3597443,0.0002408774,0.6306121,0.002232548,0.0002483776,0.00003734633,0.0006560719,0.00005409924,0.006174238],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5344952,"threshold_uncertainty_score":0.4262862,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0372318810197621,"score_gpt":0.2895079666158675,"score_spread":0.2522760855961054,"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."}}