{"id":"W3099739484","doi":"10.1109/dasc-picom-cbdcom-cyberscitech49142.2020.00083","title":"Effects of Pre-trained Word Embeddings on Text-based Deception Detection","year":2020,"lang":"en","type":"article","venue":"","topic":"Deception detection and forensic psychology","field":"Psychology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Word2vec; Computer science; Deception; Word embedding; Artificial intelligence; Word (group theory); Credibility; Natural language processing; Purchasing; F1 score; Information retrieval; Embedding; Machine learning; Mathematics; Psychology","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001267541,0.0001783597,0.0002408134,0.0001879143,0.00005298378,0.00001144427,0.0001238692,0.0002176929,0.005163901],"category_scores_gemma":[0.0001264432,0.0001652885,0.0001514861,0.0004558993,0.0001048886,0.00004927206,0.00001200835,0.0002187706,0.001425225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002981291,"about_ca_system_score_gemma":0.000009896329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001890248,"about_ca_topic_score_gemma":0.00001248087,"domain_scores_codex":[0.9986557,0.000159913,0.000333796,0.0004304077,0.0001839778,0.0002362318],"domain_scores_gemma":[0.999207,0.0001879465,0.0001638878,0.0002269745,0.00006212983,0.0001520148],"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":[0.006231014,0.0008040484,0.0003797667,0.0001277452,0.0001684894,0.00001883949,0.003075712,0.000142551,0.329655,0.003431727,0.02715054,0.6288146],"study_design_scores_gemma":[0.01822946,0.0107105,0.3743596,0.0001099755,0.0001909537,0.00004619496,0.001141236,0.005485792,0.5083795,0.00101786,0.07911541,0.001213487],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8446332,0.00002074558,0.1134791,0.001267399,0.00200267,0.000560305,0.000003476865,0.0005013403,0.03753183],"genre_scores_gemma":[0.9929763,0.000001851039,0.0003364776,0.004771118,0.0001881186,0.00006433616,0.000005019469,0.00002749305,0.001629268],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6276011,"threshold_uncertainty_score":0.9993523,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01563862207978298,"score_gpt":0.301051441334357,"score_spread":0.2854128192545741,"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."}}