{"id":"W4402159673","doi":"10.1109/icc51166.2024.10622796","title":"Subject Identification Using Behavioral Cues and Machine Learning","year":2024,"lang":"en","type":"article","venue":"","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Identification (biology); Computer science; Artificial intelligence; Subject (documents); Machine learning; World Wide Web","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006912424,0.0008027079,0.0008791835,0.002111048,0.0002302069,0.0007096302,0.0004522372,0.0005671568,0.001847782],"category_scores_gemma":[0.00214834,0.0001951067,0.0006415473,0.001175339,0.000343957,0.0008029726,0.0006549911,0.0003804008,0.001510642],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003175672,"about_ca_system_score_gemma":0.0004010874,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002517307,"about_ca_topic_score_gemma":0.003492001,"domain_scores_codex":[0.9991412,0.0002316266,0.0000505839,0.0002575663,0.0002139955,0.0001049446],"domain_scores_gemma":[0.9992049,0.0003253599,0.000128147,0.00007629816,0.0001986552,0.00006652978],"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":[0.0004656332,0.0003917747,0.03091598,0.0004318132,0.0001682397,0.0004038973,0.0002616797,0.03821507,0.05200563,0.001609541,0.003241967,0.8718888],"study_design_scores_gemma":[0.0000169005,0.0004303169,0.07809073,0.0001048192,0.0000901426,0.0006561614,0.0002887681,0.8835848,0.02574342,0.005684746,0.005232879,0.00007635965],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2659242,0.003439009,0.7132075,0.0004891694,0.0004372317,0.0003025564,0.00120405,0.004044225,0.01095206],"genre_scores_gemma":[0.8822681,0.0009901491,0.1093986,0.0002487935,0.0002264414,0.0001609303,0.001328247,0.0001303686,0.005248275],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002517307,"threshold_uncertainty_score":0.006181419,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03664372111783929,"score_gpt":0.2852658027774821,"score_spread":0.2486220816596429,"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."}}