{"id":"W4321165956","doi":"10.1371/journal.pone.0282030","title":"Eye and head movements while encoding and recognizing panoramic scenes in virtual reality","year":2023,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Eye movement; Computer science; Computer vision; Head (geology); Mirroring; Virtual reality; Encoding (memory); Artificial intelligence; Perception; Visual perception; Contrast (vision); Communication; Psychology; Neuroscience; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003300525,0.00006569104,0.0001079661,0.0001386943,0.00009684875,0.00009637819,0.00009337988,0.00003341891,0.000003437591],"category_scores_gemma":[0.00004542069,0.00006825895,0.00001032232,0.0004115264,0.00002052855,0.0003146702,0.0001506386,0.00007482093,0.00002179602],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002143393,"about_ca_system_score_gemma":0.000008031519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008041841,"about_ca_topic_score_gemma":0.0001016528,"domain_scores_codex":[0.9991899,0.00005816109,0.0001490453,0.0002674034,0.0001762721,0.0001592078],"domain_scores_gemma":[0.9997429,0.00003165514,0.0000354168,0.0001180623,0.00002431166,0.00004769979],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00003370666,0.001115992,0.3163183,0.0002939071,0.00008281152,0.00003438719,0.004733448,0.0000386028,0.4726624,0.003467894,0.00007212171,0.2011464],"study_design_scores_gemma":[0.001160646,0.0004092917,0.6123518,0.0005426126,0.00001356242,0.000002116201,0.000519787,0.3528461,0.02725136,0.004422123,0.0000751944,0.0004054116],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971935,0.00002857633,0.001782734,0.0004224101,0.00006662757,0.0001059191,0.000001725037,0.0001470211,0.0002515487],"genre_scores_gemma":[0.9990534,0.0001236796,0.0004190433,0.0001041534,0.00002018361,0.00001019238,0.000002692628,0.000004316357,0.0002622992],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4454111,"threshold_uncertainty_score":0.2783519,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.131237024689745,"score_gpt":0.3055252390729141,"score_spread":0.1742882143831691,"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."}}