{"id":"W4408738012","doi":"10.1016/j.dib.2025.111502","title":"Kenyan sign language word-based pose dataset","year":2025,"lang":"en","type":"article","venue":"Data in Brief","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Bundesministerium für Bildung und Forschung; International Development Research Centre; Styrelsen för Internationellt Utvecklingssamarbete","keywords":"Kenya; Word (group theory); Sign (mathematics); Computer science; Sign language; Natural language processing; Research article; Artificial intelligence; Linguistics; Library science; Mathematics; Biology; Ecology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006330185,0.0001034249,0.0001505576,0.0001755234,0.00004309522,0.0001889342,0.002144261,0.00005451724,0.00003926472],"category_scores_gemma":[0.0001700812,0.00009835108,0.00001430127,0.000667165,0.0000260119,0.0005125919,0.0006642138,0.0001304756,0.0001884073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002375312,"about_ca_system_score_gemma":0.0001220327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004163256,"about_ca_topic_score_gemma":0.0003672806,"domain_scores_codex":[0.9987417,0.0001361282,0.0002282046,0.0005006458,0.0001845551,0.0002087984],"domain_scores_gemma":[0.9973825,0.0001940011,0.00004833146,0.002300411,0.00002152511,0.00005327299],"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":[0.00001398482,0.0001981611,0.001935094,0.00007822135,0.00002189337,0.0002529796,0.0002555748,0.00001406266,0.0005085829,0.003088716,0.8136601,0.1799726],"study_design_scores_gemma":[0.001161097,0.00001952568,0.005833896,0.0002037355,0.000007931796,0.000006945507,0.00006415772,0.01299709,0.001189543,0.0003878005,0.9778574,0.0002709052],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009769191,0.001850319,0.9181858,0.01111589,0.003125722,0.001333481,0.04096253,0.0006703391,0.01298676],"genre_scores_gemma":[0.8654207,0.00002405675,0.04038285,0.01971316,0.000383602,0.00008352594,0.07306374,0.00002844094,0.0008999003],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8778029,"threshold_uncertainty_score":0.4010641,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02831877647269763,"score_gpt":0.3111571357547778,"score_spread":0.2828383592820802,"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."}}