{"id":"W2175802668","doi":"10.1558/sll.2003.10.1.62","title":"Earwitness identification over the telephone and in field settings","year":2003,"lang":"en","type":"article","venue":"International Journal of Speech Language and the Law","topic":"Deception detection and forensic psychology","field":"Psychology","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Identification (biology); Psychology; Test (biology); Communication; Speech recognition; Voice analysis; Field (mathematics); Computer science; Mathematics","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":[0.0022947,0.0005180874,0.0003371824,0.0007974766,0.0009170159,0.0007532702,0.0005457446,0.0008869451,0.006080856],"category_scores_gemma":[0.01110503,0.0003341107,0.0002333135,0.0002733383,0.000598987,0.001561539,0.001316797,0.0007891911,0.001136477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004866279,"about_ca_system_score_gemma":0.0004198188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002212407,"about_ca_topic_score_gemma":0.005060873,"domain_scores_codex":[0.9984274,0.0005324226,0.0001146121,0.000408775,0.0003378218,0.0001789765],"domain_scores_gemma":[0.9944211,0.001601718,0.001218447,0.000743497,0.001026309,0.0009889328],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.02749507,0.0150677,0.4497468,0.0007960693,0.0001772911,0.01088607,0.06450877,0.001477054,0.2418012,0.001617726,0.003680615,0.1827455],"study_design_scores_gemma":[0.0005850028,0.05637767,0.7925452,0.0002622091,0.0001542175,0.01284619,0.03436468,0.002545269,0.08989804,0.001661364,0.008514915,0.0002453349],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971834,0.00005933711,0.000304293,0.00001912958,0.00001200483,0.0001024198,0.00005567209,0.00001144435,0.00225225],"genre_scores_gemma":[0.9942548,0.0001187084,0.001109999,0.00006206555,0.00001293395,0.00007618548,0.000157688,0.000005861262,0.004201824],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006080856,"threshold_uncertainty_score":0.02034253,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006926003009998057,"score_gpt":0.3076604655826387,"score_spread":0.3007344625726406,"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."}}