{"id":"W2838525274","doi":"10.1109/crv.2019.00028","title":"HandSeg: An Automatically Labeled Dataset for Hand Segmentation from Depth Images","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Segmentation; Computer science; Artificial intelligence; Pattern recognition (psychology); Scale (ratio); Quality (philosophy); Market segmentation; Computer vision; Geography; Cartography","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":[0.0007571673,0.003290681,0.001550979,0.003897158,0.0008578226,0.001304332,0.002792436,0.002606469,0.01257622],"category_scores_gemma":[0.003511355,0.001045506,0.001564397,0.002457288,0.0006916892,0.001679114,0.002770528,0.001760943,0.01853875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007860977,"about_ca_system_score_gemma":0.001369164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006225539,"about_ca_topic_score_gemma":0.02184699,"domain_scores_codex":[0.9983274,0.0001696247,0.0001597353,0.0006722569,0.0004885486,0.0001824579],"domain_scores_gemma":[0.997855,0.0003885545,0.0002257347,0.0008745674,0.0004901221,0.0001660005],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001799344,0.0008094288,0.008161882,0.003397303,0.0004084264,0.0005959495,0.0003105328,0.01250267,0.07019997,0.002680758,0.5405446,0.358589],"study_design_scores_gemma":[0.0008128115,0.0009003149,0.09053317,0.001254301,0.0003810776,0.004047046,0.0007656903,0.1954778,0.1620957,0.01624793,0.5269374,0.0005468206],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.04883084,0.002666429,0.1970768,0.000428986,0.0006896843,0.001160708,0.6389951,0.09569602,0.01445547],"genre_scores_gemma":[0.05370229,0.0005294992,0.1696551,0.0002403066,0.00009400476,0.001659295,0.7677102,0.002173747,0.004235606],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01257622,"threshold_uncertainty_score":0.04207164,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04233180545687948,"score_gpt":0.3218101160723537,"score_spread":0.2794783106154742,"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."}}