{"id":"W4409166888","doi":"10.22541/au.174376812.22924392/v1","title":"Intelligent Information Retrieval Using Mobile Agents: A Proximal Policy Optimization Approach in Dynamic Networks","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Mobile Agent-Based Network Management","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"SAIT Polytechnic","funders":"","keywords":"Computer science; Artificial intelligence; Mobile agent; Distributed computing; Information retrieval","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"],"consensus_categories":[],"category_scores_codex":[0.0008837619,0.0005163183,0.000486764,0.00150709,0.0001132252,0.0007500472,0.001947286,0.0004283809,0.00003030988],"category_scores_gemma":[0.00004546111,0.0005420821,0.0001790128,0.00228637,0.00004371576,0.001036929,0.004856851,0.0007897544,0.000006864636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002479374,"about_ca_system_score_gemma":0.0007462105,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004000592,"about_ca_topic_score_gemma":0.00001162987,"domain_scores_codex":[0.9963932,0.0002572557,0.001178466,0.000889702,0.0006071759,0.0006741981],"domain_scores_gemma":[0.9975942,0.00005233155,0.000542523,0.001498702,0.0001893265,0.0001228936],"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":[0.00002639036,0.0001199789,0.0000425978,0.0002773026,0.00004668962,0.000003421614,0.0003290705,0.9770581,2.015071e-7,0.003822168,0.0001642416,0.01810981],"study_design_scores_gemma":[0.000341511,0.00004123916,0.00003530969,0.0002347223,0.00002367816,0.000001971797,0.00006360736,0.9982286,0.0000107051,0.0002109537,0.0003629241,0.0004447685],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0008056093,0.0001632321,0.9893953,0.0001135636,0.0008512367,0.003962805,0.000008176266,0.0003545487,0.00434557],"genre_scores_gemma":[0.1318659,0.0008227945,0.8633567,0.001663736,0.0002416751,0.0005501475,0.000835396,0.00003818697,0.0006254095],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1310603,"threshold_uncertainty_score":0.999703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01788770172595641,"score_gpt":0.278965848298109,"score_spread":0.2610781465721526,"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."}}