{"id":"W3174843313","doi":"10.21467/proceedings.115.22","title":"Instagram Image Filtration with Computer Vision","year":2021,"lang":"en","type":"article","venue":"AIJR Proceedings","topic":"Face recognition and analysis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University of Edmonton","funders":"","keywords":"Computer science; Upload; Face (sociological concept); Filter (signal processing); Information retrieval; Image (mathematics); Social media; Computer vision; Artificial intelligence; World Wide Web","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":[0.0008605713,0.001286813,0.001253662,0.004705639,0.0008922486,0.002906754,0.002077639,0.001259112,0.02453919],"category_scores_gemma":[0.002791977,0.0004931556,0.001742697,0.003073737,0.0006198377,0.002028632,0.00204911,0.001617908,0.01417791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001296782,"about_ca_system_score_gemma":0.001446233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006838461,"about_ca_topic_score_gemma":0.007149941,"domain_scores_codex":[0.9989109,0.00007708964,0.00004445433,0.0002950496,0.000487498,0.0001850773],"domain_scores_gemma":[0.9991554,0.0001061181,0.00005762809,0.0002956215,0.0003327682,0.00005238477],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001893102,0.000158711,0.0007571275,0.0001412152,0.00008230271,0.0000771155,0.00006599348,0.004649694,0.03169008,0.0128627,0.03132132,0.9180045],"study_design_scores_gemma":[0.00005597264,0.0002608386,0.007377779,0.00005845321,0.0001158201,0.0009735766,0.0001698606,0.5987914,0.2064051,0.02984112,0.1558145,0.0001357052],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01132029,0.0004633447,0.9570116,0.0003564563,0.0003657023,0.0002871129,0.0009344354,0.01672263,0.01253836],"genre_scores_gemma":[0.1070062,0.0004987001,0.8704644,0.0002777083,0.0002214962,0.0003888604,0.002392996,0.001088728,0.01766093],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02453919,"threshold_uncertainty_score":0.08209181,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00626581584672745,"score_gpt":0.2213691586688235,"score_spread":0.2151033428220961,"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."}}