{"id":"W4224209589","doi":"10.1016/j.eswa.2022.117231","title":"PictoBERT: Transformers for next pictogram prediction","year":2022,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Assistive Technology in Communication and Mobility","field":"Health Professions","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Pictogram; Computer science; Sentence; Transformer; Natural language processing; Artificial intelligence; Encoder; Human–computer interaction; Linguistics","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0005641532,0.0001434044,0.0002208342,0.0001094669,0.0035399,0.000009342481,0.0003645608,0.0001257283,0.0002223159],"category_scores_gemma":[0.00002197344,0.0001257889,0.00006259709,0.000389878,0.0001237048,0.00008767567,0.00008542724,0.0005331557,0.00003884879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003954272,"about_ca_system_score_gemma":0.0002187531,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002600949,"about_ca_topic_score_gemma":0.00007913911,"domain_scores_codex":[0.998327,0.0002881741,0.0004938066,0.0003472272,0.0002055603,0.0003381922],"domain_scores_gemma":[0.9983867,0.000403444,0.0002245717,0.0006966394,0.0001916416,0.00009701799],"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":[0.00140607,0.003414779,0.1394343,0.001116948,0.000820414,0.000001602491,0.0228043,0.0006919262,0.008078277,0.3392071,0.3904755,0.09254871],"study_design_scores_gemma":[0.0008673976,0.000166289,0.002142229,0.00001840612,0.00001812489,0.000005175101,0.02162303,0.001345461,0.00001178027,0.0001096814,0.9735619,0.000130454],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004374566,0.001475441,0.9462585,0.005480254,0.0006286851,0.01536857,0.0006044779,0.001394653,0.02441479],"genre_scores_gemma":[0.8274542,0.00005010784,0.001990783,0.0005499208,0.0001412199,0.1668626,0.0003424248,0.00003425619,0.002574519],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9442678,"threshold_uncertainty_score":0.9977574,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06766099040290786,"score_gpt":0.3971758998581823,"score_spread":0.3295149094552745,"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."}}