{"id":"W2024947222","doi":"10.1145/2700271","title":"GreenLocs","year":2015,"lang":"en","type":"article","venue":"ACM Transactions on Sensor Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"RSS; Computer science; Inference; Profiling (computer programming); Nonparametric statistics; Bayesian inference; Efficient energy use; Data mining; Accelerometer; Bayesian probability; Mobile device; Real-time computing; Artificial intelligence; Econometrics; World Wide Web","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":[],"consensus_categories":[],"category_scores_codex":[0.00006037115,0.0001376157,0.0001211901,0.0001042284,0.00006755461,0.00002518437,0.0002029297,0.0001739021,0.00006465071],"category_scores_gemma":[0.00001724447,0.0001349551,0.00005621255,0.0003156485,0.000040994,0.00007868169,0.000002679173,0.0002726726,0.0001502054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000584117,"about_ca_system_score_gemma":0.000006945136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007532374,"about_ca_topic_score_gemma":0.00001446048,"domain_scores_codex":[0.9993488,0.00001359873,0.0001494203,0.000126592,0.000127222,0.0002344005],"domain_scores_gemma":[0.9993582,0.00004849209,0.00001174249,0.0004495288,0.00004711399,0.00008492667],"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":[0.000008438215,0.00001248737,0.00003574035,0.000003455954,0.00002269539,0.000005700385,0.00005870762,0.9450555,0.00001387112,0.00007843931,0.002305061,0.05239986],"study_design_scores_gemma":[0.001269731,0.0001809593,0.0001715725,0.00004265585,0.0000688717,0.00005763018,0.001158875,0.9122404,0.01074602,0.001612548,0.07168392,0.000766798],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006850615,0.0001729417,0.9864746,0.0001735849,0.000876038,0.0001026394,0.000006021822,0.001919884,0.003423715],"genre_scores_gemma":[0.9947314,0.0001441541,0.004448027,0.0001324697,0.00008544915,0.00001581352,0.000005877233,0.00003834654,0.0003984445],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9878808,"threshold_uncertainty_score":0.5503309,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02599916013742729,"score_gpt":0.2237032587691147,"score_spread":0.1977040986316874,"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."}}