{"id":"W3210385438","doi":"10.1007/s12650-021-00809-4","title":"Neural network training fingerprint: visual analytics of the training process in classification neural networks","year":2021,"lang":"en","type":"article","venue":"Journal of Visualization","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Artificial neural network; Computer science; Process (computing); Artificial intelligence; Visualization; Fingerprint (computing); Machine learning; Training (meteorology); Visual analytics; Analytics; Time delay neural network; Pattern recognition (psychology); Data mining","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":[0.0008302256,0.0001452289,0.0003374205,0.0001852088,0.0001125033,0.0001767317,0.000624094,0.0000935636,0.0000109047],"category_scores_gemma":[0.0004765691,0.0001191125,0.0001400896,0.002464156,0.00005352924,0.0007556077,0.0001118491,0.0002673548,2.914236e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005336877,"about_ca_system_score_gemma":0.0003191589,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001231362,"about_ca_topic_score_gemma":0.0000158069,"domain_scores_codex":[0.9975942,0.0003160952,0.001053457,0.0002109614,0.0005594835,0.0002657424],"domain_scores_gemma":[0.9977472,0.0001361006,0.001143808,0.0002522011,0.0006395197,0.00008114372],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001513824,0.0001600823,0.01524994,0.00003955181,0.00003545878,0.00001825256,0.004659312,0.9372532,0.0003414371,0.02025855,0.0001569254,0.02181217],"study_design_scores_gemma":[0.0004037374,0.00006434406,0.01411502,0.0001654568,0.00002812413,0.00006070697,0.0009574923,0.9831375,0.0002518602,0.0005665511,0.0001312181,0.0001180104],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1606666,0.0002057793,0.8375586,0.0005603014,0.0007891826,0.00008811963,0.000001265958,0.00002102761,0.0001091675],"genre_scores_gemma":[0.998105,0.0000663499,0.001065184,0.0004352932,0.000277994,0.000001062003,0.00001278547,0.00001450811,0.00002183001],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8374385,"threshold_uncertainty_score":0.4857265,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06650849845540834,"score_gpt":0.3517488066064046,"score_spread":0.2852403081509962,"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."}}