{"id":"W3157441733","doi":"10.24908/iqurcp.7227","title":"The Activation of Scene Gist: Global Versus Local Features","year":2017,"lang":"en","type":"article","venue":"Inquiry Queen s Undergraduate Research Conference Proceedings","topic":"Aesthetic Perception and Analysis","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Scene statistics; Computer science; Computer vision; Artificial intelligence; GiST; Space (punctuation); Perception; Psychology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003298092,0.0001974038,0.0001964919,0.0002521052,0.0001653678,0.0007198896,0.0001601727,0.0004080803,0.003959657],"category_scores_gemma":[0.003142888,0.0002251723,0.000167605,0.0001666785,0.0005175259,0.001052665,0.0006547113,0.0003981297,0.0002017172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002023012,"about_ca_system_score_gemma":0.000159925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006110382,"about_ca_topic_score_gemma":0.0009284751,"domain_scores_codex":[0.9997713,0.00005493366,0.000006986243,0.00009033256,0.00004710377,0.00002933533],"domain_scores_gemma":[0.9989994,0.0005647673,0.0002064695,0.00007301631,0.00008510727,0.00007119468],"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":[0.001845948,0.0001055044,0.0363074,0.0006615642,0.00006103389,0.0004383549,0.01954299,0.0007687021,0.8436932,0.003725434,0.0008561069,0.0919937],"study_design_scores_gemma":[0.0001427313,0.001328175,0.8905387,0.0002220891,0.0001559504,0.0011639,0.01488259,0.008087864,0.06174878,0.01316357,0.008476418,0.00008931973],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9860195,0.000214627,0.0055814,0.0001116042,0.00001487029,0.00002642496,0.00006990523,0.00003331322,0.007928239],"genre_scores_gemma":[0.9957742,0.0001244994,0.003091072,0.00003764028,0.000006569771,0.00002742767,0.00005820619,0.0000326349,0.000847757],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003959657,"threshold_uncertainty_score":0.01324636,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1900970450279137,"score_gpt":0.4320648789005478,"score_spread":0.2419678338726341,"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."}}