{"id":"W3211547869","doi":"","title":"Big data meet deep data: Characterizing spatial navigation in hippocampal amnesia","year":2021,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Baycrest Hospital; York University","funders":"","keywords":"Computer science; Amnesia; Big data; Hippocampal formation; Artificial intelligence; Neuroscience; Data mining; Cognitive psychology; Psychology","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":[0.0003728207,0.000296705,0.0002456494,0.0007201199,0.000181302,0.0005669119,0.0004123515,0.0003656704,0.0008935708],"category_scores_gemma":[0.002651231,0.0001698692,0.0002292854,0.000597484,0.000269503,0.0005544865,0.0005661969,0.000523741,0.0001614968],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003390415,"about_ca_system_score_gemma":0.0004216422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008004445,"about_ca_topic_score_gemma":0.01118004,"domain_scores_codex":[0.9999214,0.00001706889,0.000006951189,0.00001907626,0.0000171827,0.00001833619],"domain_scores_gemma":[0.9993118,0.0002360862,0.0001175428,0.0001190918,0.00009352152,0.0001218796],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003560873,0.0006567852,0.683175,0.0003096685,0.0004518536,0.001113095,0.0008886565,0.01888504,0.05240959,0.003566839,0.01528045,0.2197021],"study_design_scores_gemma":[0.00009268188,0.0004242774,0.6915078,0.00006567882,0.0001986064,0.00140541,0.001478896,0.2387706,0.02047258,0.04187075,0.003656141,0.00005663893],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9874979,0.000410171,0.008988685,0.0005307642,0.00003471518,0.00001997261,0.001979582,0.0001097477,0.0004284733],"genre_scores_gemma":[0.9947673,0.0001282285,0.003600173,0.00004518873,0.00001496819,0.00001737279,0.001091204,0.0000211587,0.0003145025],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008004445,"threshold_uncertainty_score":0.01591575,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05420562393424952,"score_gpt":0.2775438625599216,"score_spread":0.2233382386256721,"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."}}