{"id":"W4313471539","doi":"10.3390/s23010432","title":"SoftSAR: The New Softer Side of Socially Assistive Robots—Soft Robotics with Social Human–Robot Interaction Skills","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Sciences North; Baycrest Hospital; Toronto Rehabilitation Institute; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; AGE-WELL","keywords":"Soft robotics; Robot; Robotics; Artificial intelligence; Human–computer interaction; Field (mathematics); Soft materials; Perspective (graphical); Computer science; Human–robot interaction; Engineering; Nanotechnology; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001417605,0.0009092474,0.0005206272,0.0008540766,0.001463334,0.003209577,0.001248845,0.002216687,0.01330165],"category_scores_gemma":[0.002356771,0.0003966903,0.0005992137,0.000374903,0.009950252,0.006858372,0.005391262,0.002344421,0.003004641],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007025556,"about_ca_system_score_gemma":0.001049167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003093495,"about_ca_topic_score_gemma":0.0005227697,"domain_scores_codex":[0.9988128,0.0004204713,0.00005014448,0.0001934232,0.0004047263,0.0001185329],"domain_scores_gemma":[0.9979651,0.0008506178,0.0002289156,0.0003623254,0.0002026019,0.0003904386],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006926178,0.000137213,0.0008240842,0.001054993,0.00004357684,0.000286066,0.002391482,0.003546912,0.02235674,0.8070711,0.01120322,0.1510154],"study_design_scores_gemma":[0.00003910743,0.0005695402,0.001747152,0.0006835795,0.00003714695,0.001352508,0.002723654,0.02141225,0.01816175,0.5979311,0.3551832,0.0001589274],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04701526,0.01268197,0.5860982,0.0253841,0.00211872,0.0002409078,0.0001346838,0.001588234,0.3247379],"genre_scores_gemma":[0.6230515,0.01130424,0.2652625,0.007707766,0.001828816,0.0006048346,0.0001752503,0.0004117087,0.08965331],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01330165,"threshold_uncertainty_score":0.04449844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01262861506384476,"score_gpt":0.2394072148267864,"score_spread":0.2267785997629416,"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."}}