{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0004906546,0.0003119744,0.0004664573,0.0001282877,0.0001123132,0.001457782,0.0011786,0.0002761465,0.0000772638],"category_scores_gemma":[0.00006304032,0.0002608119,0.00009441139,0.00009159688,0.00003347726,0.0006059945,0.0006707381,0.0002078745,0.0003285719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005191768,"about_ca_system_score_gemma":0.000213787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002105153,"about_ca_topic_score_gemma":0.000275713,"domain_scores_codex":[0.9976594,0.0001848383,0.0005097779,0.0009681319,0.0003949665,0.0002829032],"domain_scores_gemma":[0.9976358,0.0003491548,0.0002846064,0.001347335,0.0002227714,0.0001603535],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001680917,0.001438933,0.00298929,0.002430653,0.001588986,0.00005631549,0.006750325,0.002050559,0.0537052,0.004033949,0.5206885,0.4040992],"study_design_scores_gemma":[0.007418211,0.0006783401,0.006592901,0.001000749,0.0003260421,0.00003128044,0.0003403228,0.7868213,0.1416786,0.02376707,0.02847049,0.002874713],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005962492,0.00008162681,0.9840122,0.0004691217,0.001312238,0.00152427,0.005916053,0.0002507208,0.0004712918],"genre_scores_gemma":[0.1950993,0.00002036642,0.7455485,0.001074207,0.0005497329,0.0006075102,0.05612843,0.00004767976,0.0009243132],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7847708,"threshold_uncertainty_score":0.9999844,"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."}}