{"id":"W4393194726","doi":"10.2139/ssrn.4772832","title":"A Continuous Authentication Approach for Mobile Crowdsourcing Based on Federated Learning","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Crowdsourcing; Authentication (law); Computer science; World Wide Web; Computer security","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":["metaepi_narrow","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.004450507,0.0005505624,0.0005818142,0.0005136994,0.0008691957,0.002271424,0.001042902,0.000413662,0.000003223659],"category_scores_gemma":[0.0001696685,0.0005161128,0.0005280537,0.0003609233,0.00005146823,0.0001111902,0.0004633394,0.007879102,0.0000235767],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001552754,"about_ca_system_score_gemma":0.004042771,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000314549,"about_ca_topic_score_gemma":0.00001143696,"domain_scores_codex":[0.9945726,0.0003250072,0.0006728689,0.001139791,0.0005691887,0.002720567],"domain_scores_gemma":[0.9980811,0.0002370442,0.0005373046,0.0006567031,0.0003257162,0.0001620841],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001648923,0.0004434206,0.0001073642,0.000635093,0.000661848,0.00002584976,0.00214267,0.7303974,0.002215672,0.07187928,0.0005154283,0.1908111],"study_design_scores_gemma":[0.0005844585,0.0005997277,0.000006674276,0.0004057986,0.0001127704,0.0002075795,0.0006845684,0.9368792,0.0004826857,0.05875904,0.0007291538,0.0005483854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03709688,0.002198895,0.9568849,0.0004554064,0.0008965751,0.0008979127,0.000003566174,0.0005565715,0.001009338],"genre_scores_gemma":[0.9857967,0.0001057811,0.01054438,0.0001157402,0.000640016,0.0002416854,0.00004716081,0.0001090528,0.002399496],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9486998,"threshold_uncertainty_score":0.999729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009274806273213233,"score_gpt":0.2438275767950636,"score_spread":0.2345527705218504,"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."}}