{"id":"W4403587518","doi":"10.1177/02783649241274794","title":"THÖR-MAGNI: A large-scale indoor motion capture recording of human movement and robot interaction","year":2024,"lang":"en","type":"article","venue":"The International Journal of Robotics Research","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Knut och Alice Wallenbergs Stiftelse","keywords":"Robot; Motion (physics); Computer science; Motion capture; Movement (music); Scale (ratio); Computer vision; Artificial intelligence; Human–computer interaction; Communication; Psychology; Physics; Acoustics","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":[],"consensus_categories":[],"category_scores_codex":[0.002014112,0.00007509868,0.0001149268,0.0005501811,0.0001348139,0.0004325008,0.0006785324,0.00004501582,0.00006332385],"category_scores_gemma":[0.00008533912,0.0000532156,0.00008393472,0.0002487237,0.00005800523,0.0006971224,0.0002696163,0.0006342408,0.00001316026],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001523049,"about_ca_system_score_gemma":0.00007483497,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004712057,"about_ca_topic_score_gemma":0.0000362579,"domain_scores_codex":[0.9981244,0.0001646912,0.0003921583,0.0001436587,0.001016478,0.0001586818],"domain_scores_gemma":[0.9984509,0.0002669495,0.000161349,0.000132826,0.0009311882,0.00005672798],"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":[0.000378543,0.001485444,0.003238228,0.0004018312,0.001699188,0.0005929308,0.02779316,0.03065286,0.2760929,0.2334452,0.02616236,0.3980573],"study_design_scores_gemma":[0.002508328,0.001391769,0.006310239,0.00303815,0.0001026162,0.001331572,0.007051334,0.681847,0.08995144,0.1980935,0.00782702,0.0005470848],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2794278,0.000733603,0.6892983,0.02459933,0.003350937,0.0002726342,0.00001042098,0.000041886,0.00226502],"genre_scores_gemma":[0.9960262,0.0001981583,0.00283604,0.0001082471,0.0003992676,0.000002559003,0.000003037355,0.000008033247,0.0004185153],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7165983,"threshold_uncertainty_score":0.4170615,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07176123491208583,"score_gpt":0.391955749896926,"score_spread":0.3201945149848402,"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."}}