{"id":"W4389371651","doi":"10.17072/2619-0648-2022-3-116-127","title":"DIGITAL VIDEO IMAGES IN FORENSIC IDENTIFICATION","year":2022,"lang":"en","type":"article","venue":"Ex Jure","topic":"Security, Politics, and Digital Transformation","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"McGill University","keywords":"Computer science; Identification (biology); Relevance (law); Reliability (semiconductor); Digital video; Artificial intelligence; Software; Mode (computer interface); Digital forensics; Multimedia; Computer vision; Frame (networking); Human–computer interaction; Computer security; Telecommunications","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000238136,0.00004257455,0.00005347735,0.00007003218,0.0002396489,0.0001871293,0.0001221449,0.00002314483,0.0001263195],"category_scores_gemma":[0.0001017067,0.00004926899,0.00003472232,0.0001431638,0.00008515082,0.0007798496,0.00002148636,0.00008298272,0.00006031152],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001196422,"about_ca_system_score_gemma":0.00006838059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002444503,"about_ca_topic_score_gemma":0.0004782614,"domain_scores_codex":[0.9992787,0.0000406278,0.0001393808,0.00009798022,0.0002767142,0.0001665473],"domain_scores_gemma":[0.9997818,0.0000410532,0.00003303442,0.00007610521,0.00002762337,0.00004037576],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003536671,0.0003978548,0.04241935,0.00004458704,0.00001398031,0.00001905638,0.141903,0.0001137966,0.0002006409,0.6874642,0.04165098,0.08573719],"study_design_scores_gemma":[0.0004623507,0.00004693681,0.0473243,0.000009964194,0.000006884141,0.000002835261,0.04637963,0.00005392103,0.0006167464,0.1314876,0.773333,0.0002758731],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.4485408,0.0001612449,0.0001324902,0.00242269,0.0005799995,0.0002472079,0.0001110449,0.00009427669,0.5477102],"genre_scores_gemma":[0.9949614,0.00001019938,0.000008257515,0.0001598727,0.0001098666,0.00001510839,0.00006036753,0.00000446674,0.004670434],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.731682,"threshold_uncertainty_score":0.2009131,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01986181625496826,"score_gpt":0.295707677231351,"score_spread":0.2758458609763828,"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."}}