{"id":"W6902136363","doi":"10.6084/m9.figshare.14950935.v1","title":"Xforce Keygen Vehicle Tracking 2019","year":2021,"lang":"fa","type":"article","venue":"Figshare","topic":"Robotic Process Automation Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Tracking (education); Vehicle tracking system; Object (grammar); Video tracking; Intersection (aeronautics); Product (mathematics); Visual Basic for Applications; BitTorrent tracker","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00003225415,0.0002133925,0.0002066973,0.00005411563,0.0001377397,0.0002273409,0.00029855,0.0001849268,0.324396],"category_scores_gemma":[0.0005794117,0.0002757391,0.0001020234,0.0005360878,0.000004351723,0.000294292,0.0001250781,0.0002742987,0.0375413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001233054,"about_ca_system_score_gemma":0.0001650411,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003937862,"about_ca_topic_score_gemma":0.000006759328,"domain_scores_codex":[0.9987044,0.00002298441,0.0003327848,0.0003330588,0.0002387137,0.0003680424],"domain_scores_gemma":[0.9988549,0.0001255702,0.00008500426,0.0005351945,0.0002560588,0.000143228],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00000103928,0.00006122312,0.00002455239,0.001150703,0.00008791337,0.00004498577,0.0005582278,0.07577731,0.002853816,0.0004704087,0.8889454,0.03002442],"study_design_scores_gemma":[0.0005038127,0.0000110869,0.01143773,0.003182402,0.00004934739,0.00004660871,0.0001617031,0.328217,0.03111631,0.0002273985,0.6242872,0.0007593932],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.001565639,0.03694664,0.004746054,0.004177066,0.0009938033,0.001640564,0.7666048,0.0039629,0.1793625],"genre_scores_gemma":[0.6880016,0.0001191497,0.003771559,0.0007535116,0.0008407478,0.000646577,0.2904252,0.0002810759,0.01516062],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.686436,"threshold_uncertainty_score":0.9999695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03066406429977299,"score_gpt":0.2536756353700526,"score_spread":0.2230115710702796,"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."}}