{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003832465,0.0003030798,0.0003466056,0.0003513665,0.0001720694,0.0003442086,0.0004934344,0.0002552389,0.0005309303],"category_scores_gemma":[0.0006356292,0.0002758138,0.0002037771,0.0003738459,0.000223622,0.0001240163,0.0005209251,0.000594576,0.0001442323],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002102974,"about_ca_system_score_gemma":0.00009301461,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006009305,"about_ca_topic_score_gemma":0.001313511,"domain_scores_codex":[0.9975479,0.0002682502,0.0003299484,0.0009975197,0.000424483,0.0004318733],"domain_scores_gemma":[0.9988883,0.00002903447,0.0002292485,0.0006616815,0.00007296135,0.0001188185],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00008027947,0.0004490235,0.002808729,0.000270549,0.00001133967,0.000148564,0.002515921,0.001347845,0.8808575,0.0001776828,0.004248249,0.1070843],"study_design_scores_gemma":[0.002640137,0.0003712843,0.02278184,0.001853262,0.0002285589,0.0001494346,0.005326568,0.3409764,0.6063526,0.004084026,0.01060252,0.004633387],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9725556,0.00004077131,0.01217181,0.004079328,0.0004171789,0.0003428662,0.0000386837,0.0003098215,0.01004391],"genre_scores_gemma":[0.9804408,0.0001186955,0.0008979073,0.0004903685,0.000168607,0.00002856785,0.000008886072,0.00003043282,0.01781575],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3396285,"threshold_uncertainty_score":0.9999694,"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."}}