{"id":"W2911358689","doi":"10.5430/air.v8n1p1","title":"Hybrid human resources localization and tracking system","year":2019,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"IoT and GPS-based Vehicle Safety Systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Radio-frequency identification; Software deployment; Python (programming language); Real-time computing; Global Positioning System; Human resources; Software engineering; Telecommunications; Computer security; Operating system","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.00140726,0.0001215098,0.0001713292,0.0002491386,0.0002624915,0.0001919607,0.000197151,0.00007749657,0.00006703174],"category_scores_gemma":[0.00003525179,0.0001209333,0.00003518158,0.0003513691,0.0001039645,0.0001679536,0.00004723196,0.0003156139,0.0007137114],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001168165,"about_ca_system_score_gemma":0.00001488103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002391613,"about_ca_topic_score_gemma":0.00005187596,"domain_scores_codex":[0.9983277,0.000145997,0.0003550448,0.0002623689,0.0004402942,0.0004686367],"domain_scores_gemma":[0.9992651,0.0001702167,0.00002164977,0.0002672303,0.0001680441,0.0001078111],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001442267,0.0001271305,0.01276267,0.003731832,0.0001399943,0.0001230068,0.00984497,0.1284227,0.2122525,0.1321623,0.00146896,0.4988197],"study_design_scores_gemma":[0.0000448557,0.0001539139,0.0002507351,0.0004057196,0.00000571194,0.00002023595,0.005263905,0.7565495,0.2287475,0.003441139,0.004794376,0.0003223769],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.976778,0.0003930236,0.01564996,0.00005499288,0.0003314005,0.0004355298,0.000003763384,0.0002762337,0.00607711],"genre_scores_gemma":[0.9994803,0.00002633004,0.000017056,0.000004781436,0.0002633281,0.00001661646,0.000006309253,0.00003547545,0.000149783],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6281268,"threshold_uncertainty_score":0.9173554,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08193858878035407,"score_gpt":0.3430623408392712,"score_spread":0.2611237520589171,"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."}}