{"id":"W4307136941","doi":"10.1145/3517428.3544805","title":"AAC with Automated Vocabulary from Photographs: Insights from School and Speech-Language Therapy Settings","year":2022,"lang":"en","type":"article","venue":"","topic":"Assistive Technology in Communication and Mobility","field":"Health Professions","cited_by":69,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; AGE-WELL","keywords":"Computer science; Vocabulary; Natural language processing; Artificial intelligence; Linguistics","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":[0.003197014,0.0006652606,0.000384564,0.00153531,0.003038654,0.004290825,0.00147972,0.001862038,0.003194518],"category_scores_gemma":[0.01548355,0.0003627009,0.0003866114,0.0006751427,0.005595385,0.004030066,0.003318248,0.001735438,0.0007677148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00190887,"about_ca_system_score_gemma":0.001179263,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005066991,"about_ca_topic_score_gemma":0.01489723,"domain_scores_codex":[0.9951456,0.003626081,0.000135664,0.0003046989,0.0004952693,0.0002925944],"domain_scores_gemma":[0.9904791,0.007702096,0.0004694065,0.0003683409,0.0005847061,0.000396269],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.00007151633,0.0002025028,0.007789823,0.0003523042,0.00001364041,0.002604974,0.942999,0.0004748205,0.004154838,0.004564232,0.002366428,0.03440579],"study_design_scores_gemma":[0.0000294416,0.0002016142,0.01144185,0.0004095029,0.00002805575,0.003624995,0.8978073,0.002357207,0.004204975,0.004967405,0.07485882,0.00006885761],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9440498,0.0009130395,0.02156212,0.00249162,0.00004699243,0.000225183,0.0002301106,0.0001850426,0.03029594],"genre_scores_gemma":[0.9835278,0.0006138129,0.009723019,0.0003098612,0.00001675947,0.00009849035,0.0001536343,0.00009817517,0.005458438],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005066991,"threshold_uncertainty_score":0.01690763,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02382225144825723,"score_gpt":0.3553437351339162,"score_spread":0.331521483685659,"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."}}