{"id":"W4253347597","doi":"10.31234/osf.io/zx2gs","title":"How low can you go? Detecting style in extremely low resolution images","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Aesthetic Perception and Analysis","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Artificial intelligence; Computer science; Low resolution; Computer vision; Pixel; Context (archaeology); Pattern recognition (psychology); Resolution (logic); Coding (social sciences); Visual Objects; Psychology; High resolution; Mathematics; Perception; 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":[0.0005345044,0.0002656061,0.0003223661,0.0004243048,0.0003105563,0.001528137,0.0003919852,0.0007928046,0.002816545],"category_scores_gemma":[0.006573958,0.000283069,0.0002279201,0.0002466826,0.0009050205,0.002720468,0.0005873409,0.0008870478,0.0009136351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003369836,"about_ca_system_score_gemma":0.0001094495,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006592303,"about_ca_topic_score_gemma":0.0009415146,"domain_scores_codex":[0.9997504,0.00005341936,0.00001106143,0.00009092799,0.00005848919,0.00003559705],"domain_scores_gemma":[0.9992309,0.0002729829,0.0001326052,0.0001448761,0.0001243356,0.00009438771],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001692594,0.000184529,0.08452093,0.0005876307,0.0002687277,0.0004898286,0.002291789,0.003313024,0.4742704,0.0171557,0.01085226,0.4043726],"study_design_scores_gemma":[0.0001169251,0.0008415337,0.5592568,0.0003297304,0.0003229932,0.003132766,0.003950704,0.04234396,0.2042059,0.1629819,0.0222482,0.0002685635],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9211814,0.001937355,0.0534506,0.004056094,0.0002279706,0.00003739363,0.0003646031,0.0005052182,0.01823926],"genre_scores_gemma":[0.9676102,0.0005367515,0.02904474,0.000707079,0.00004471246,0.00001597197,0.0001545657,0.000100733,0.001785117],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002816545,"threshold_uncertainty_score":0.009422302,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04543143193455431,"score_gpt":0.27986443475273,"score_spread":0.2344330028181757,"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."}}