{"id":"W3164416780","doi":"10.1038/s41598-021-91006-8","title":"Gravitational effects of scene information in object localization","year":2021,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"College of Family Physicians of Canada; University of Toronto","funders":"National Eye Institute; National Institutes of Health","keywords":"Artificial intelligence; Computer vision; Scene statistics; Luminance; Landmark; Computer science; Context (archaeology); Object (grammar); Pattern recognition (psychology); Perception; Mathematics; Psychology; Geography","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":[0.0005149465,0.0003262824,0.0002140973,0.0004719637,0.000189156,0.0004250071,0.0003082431,0.000218485,0.001586111],"category_scores_gemma":[0.006225285,0.0002088323,0.0002146078,0.0003274644,0.0006513166,0.0005141907,0.0006800189,0.0003092943,0.0002061697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005221016,"about_ca_system_score_gemma":0.0002679394,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002646854,"about_ca_topic_score_gemma":0.002500639,"domain_scores_codex":[0.99962,0.0001286008,0.00001665077,0.00007134319,0.0001215203,0.00004186681],"domain_scores_gemma":[0.9983498,0.0008886363,0.0003044558,0.0002073877,0.0001433591,0.000106418],"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":[0.0009920333,0.0000745591,0.01626073,0.0002466356,0.0000828066,0.0001976951,0.0005407842,0.009672108,0.885626,0.007068624,0.0004817981,0.0787563],"study_design_scores_gemma":[0.0001505878,0.001280336,0.7675571,0.00005614823,0.000199682,0.000586604,0.0003681067,0.07202069,0.1352272,0.01897045,0.00348117,0.0001019169],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9723689,0.0007899405,0.02089105,0.000171843,0.00002741319,0.00002467214,0.00006420814,0.0001978789,0.005464155],"genre_scores_gemma":[0.9953754,0.0001825364,0.003803552,0.00004831776,0.00001505364,0.000008652106,0.00005569859,0.00004415081,0.0004665339],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002646854,"threshold_uncertainty_score":0.005306065,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007641548427258697,"score_gpt":0.2506054034182297,"score_spread":0.242963854990971,"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."}}