{"id":"W4402557614","doi":"10.1109/tpami.2024.3462291","title":"Quantifying and Learning Static vs. Dynamic Information in Deep Spatiotemporal Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada); University of Guelph; Ontario Tech University; York University","funders":"","keywords":"Computer science; Artificial intelligence; Deep learning; Machine learning","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":[0.002673735,0.001204738,0.0006211897,0.0008360632,0.0004502847,0.001164836,0.00107351,0.001086377,0.0007377129],"category_scores_gemma":[0.01241423,0.0006600014,0.0005500952,0.0008926629,0.001055251,0.003799482,0.001737003,0.002193511,0.0002230022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001450667,"about_ca_system_score_gemma":0.001120447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006563916,"about_ca_topic_score_gemma":0.01177256,"domain_scores_codex":[0.9993191,0.0001873961,0.00004392001,0.0002080353,0.0001179919,0.0001236371],"domain_scores_gemma":[0.9972817,0.001441175,0.0003863595,0.0004433457,0.0003175696,0.0001298677],"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":[0.0004154979,0.000106557,0.02271461,0.0001198517,0.0001705016,0.0001210604,0.0002760139,0.8144808,0.01328803,0.01621635,0.001538728,0.1305519],"study_design_scores_gemma":[0.000005304556,0.00003433554,0.00153362,0.00001532201,0.00001670018,0.00002381674,0.00002003261,0.9818155,0.002940689,0.01335003,0.0002353757,0.000009200559],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3544329,0.001031849,0.6398478,0.001323005,0.00006605806,0.0000461977,0.0005684217,0.0008135899,0.001870148],"genre_scores_gemma":[0.971185,0.0003406282,0.02668324,0.0001373128,0.00004344961,0.00002948734,0.0005452939,0.00006954977,0.0009659991],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006563916,"threshold_uncertainty_score":0.01414019,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02018833194401566,"score_gpt":0.3023741369426208,"score_spread":0.2821858049986051,"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."}}