{"id":"W4399771149","doi":"10.1371/journal.pone.0296985","title":"CAManim: Animating end-to-end network activation maps","year":2024,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Newborn Screening Ontario; Institute for Clinical Evaluative Sciences; University of Ottawa; Ottawa Hospital; Children's Hospital of Eastern Ontario","funders":"","keywords":"Computer science; Convolutional neural network; End-to-end principle; Artificial intelligence; Visualization; Focus (optics); Metric (unit); Deep learning; Data science; Machine learning; Information retrieval","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004282772,0.0001352977,0.0001554911,0.0001266923,0.0001888293,0.0005219589,0.0005700908,0.00005545949,0.0001163626],"category_scores_gemma":[0.000154078,0.0001400792,0.00003905164,0.001064458,0.0000205681,0.0008775177,0.0002617161,0.0002029984,0.001383697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001182809,"about_ca_system_score_gemma":0.00006764768,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001007859,"about_ca_topic_score_gemma":0.00004088852,"domain_scores_codex":[0.9983365,0.00005730128,0.0002586573,0.0004566322,0.0004599032,0.0004310145],"domain_scores_gemma":[0.9990553,0.0002551317,0.00004465121,0.0004346941,0.00009794692,0.0001122355],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001852639,0.0006072128,0.0005440083,0.0003552994,0.0002488023,0.0001215478,0.007843934,0.003349431,0.2743984,0.6117973,0.01053699,0.09017853],"study_design_scores_gemma":[0.00004611227,0.0002474955,0.0003878096,0.001040897,0.00003375273,0.000005493175,0.0002118039,0.3992812,0.5547279,0.03870764,0.00482735,0.0004826008],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2971404,0.0003881036,0.67099,0.008684849,0.000601701,0.0007542819,0.000005726053,0.001590031,0.01984484],"genre_scores_gemma":[0.9105511,0.00001050253,0.08719107,0.000597622,0.0005811586,0.00005066969,0.000004719916,0.00002273337,0.0009904843],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6134106,"threshold_uncertainty_score":0.9993938,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07015756192468069,"score_gpt":0.2623155234293532,"score_spread":0.1921579615046725,"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."}}