{"id":"W2787598613","doi":"10.1109/aciiw.2017.8272586","title":"Multimodal cross-context recognition of negative interactions","year":2017,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Transport Canada","funders":"","keywords":"Computer science; Classifier (UML); Context (archaeology); Artificial intelligence; Perspective (graphical); Modality (human–computer interaction); Set (abstract data type); Context model; Speech recognition; Feature (linguistics); Machine learning; Human–computer interaction; Object (grammar)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006042007,0.0007953061,0.0006719384,0.0008524083,0.0003040672,0.0007040895,0.0003688487,0.0005911069,0.002962773],"category_scores_gemma":[0.002433942,0.0001566089,0.0004642159,0.0002832756,0.0002926535,0.0007535617,0.001304811,0.0004806237,0.0009369762],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002101867,"about_ca_system_score_gemma":0.0001999044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001075727,"about_ca_topic_score_gemma":0.002148901,"domain_scores_codex":[0.9993805,0.0001335213,0.00002614718,0.0001940458,0.0001510962,0.0001147956],"domain_scores_gemma":[0.9992843,0.0002696927,0.00008531043,0.00009550607,0.0001970749,0.0000681528],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002065285,0.0003963285,0.03849018,0.0007162478,0.0002793325,0.001956017,0.001247526,0.01591117,0.3590829,0.0008346812,0.003909035,0.5751112],"study_design_scores_gemma":[0.00005964004,0.002503438,0.4054269,0.0003152226,0.0005426703,0.005681577,0.002624228,0.3916684,0.1750843,0.00463351,0.01119373,0.0002664892],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.876758,0.002560606,0.107086,0.0001907171,0.000316576,0.0002030275,0.0009320804,0.001913862,0.01003905],"genre_scores_gemma":[0.9825386,0.0003340358,0.01529251,0.0000718471,0.00005572867,0.00006708359,0.0003410866,0.00005835721,0.001240781],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002962773,"threshold_uncertainty_score":0.009911418,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0477614163353707,"score_gpt":0.3402594354886167,"score_spread":0.292498019153246,"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."}}