{"id":"W4366729881","doi":"10.46254/sa03.20220257","title":"Automatic Clothes Retriever (ACR)","year":2023,"lang":"en","type":"article","venue":"","topic":"Image Processing and 3D Reconstruction","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Clothing; Computer science; Labrador Retriever; Computer vision; Medicine; Geography; Archaeology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0004154991,0.001274828,0.0008130043,0.001229936,0.0002868265,0.0006106488,0.001215245,0.0007411018,0.02053378],"category_scores_gemma":[0.001008404,0.0004446838,0.0005508302,0.0005821391,0.0001909502,0.0007988826,0.0007707796,0.0003979687,0.00834187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002293938,"about_ca_system_score_gemma":0.00019089,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008481127,"about_ca_topic_score_gemma":0.00103741,"domain_scores_codex":[0.9991736,0.0000629324,0.00005692999,0.0002542239,0.000386226,0.00006602539],"domain_scores_gemma":[0.9992915,0.0001205378,0.00007758279,0.0001514509,0.0003017152,0.00005713312],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002229228,0.0003776965,0.009179902,0.00174587,0.0001445778,0.001135863,0.0003335303,0.001192873,0.3891425,0.0009503845,0.04876463,0.5448029],"study_design_scores_gemma":[0.0004541839,0.002150567,0.08472187,0.0002925884,0.0004385165,0.008839724,0.0002984622,0.09095085,0.5916694,0.0008097591,0.2189429,0.0004313043],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3120474,0.006343228,0.4440314,0.0005969299,0.001368184,0.00223955,0.008992057,0.1659075,0.05847377],"genre_scores_gemma":[0.753098,0.001386408,0.1643147,0.001149155,0.0003057225,0.0008124472,0.006616842,0.003307091,0.06900959],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02053378,"threshold_uncertainty_score":0.06869233,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01365438916657336,"score_gpt":0.2376738351768448,"score_spread":0.2240194460102714,"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."}}