{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007487715,0.000981884,0.0008317445,0.001156835,0.0004978986,0.001243788,0.002088569,0.001057158,0.01737112],"category_scores_gemma":[0.003925985,0.0004810008,0.0006942251,0.000975653,0.0004889057,0.002430743,0.003030041,0.0009120204,0.01309774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000504599,"about_ca_system_score_gemma":0.000803658,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003882768,"about_ca_topic_score_gemma":0.007530088,"domain_scores_codex":[0.9992524,0.0001170187,0.00003042456,0.0002323965,0.0002854948,0.0000823037],"domain_scores_gemma":[0.9986371,0.00031284,0.0001397307,0.000478306,0.0002921234,0.0001397442],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001511763,0.0002592408,0.01669997,0.0007591282,0.0002149485,0.0006317405,0.0008928021,0.05092929,0.01592742,0.02756829,0.167028,0.7175773],"study_design_scores_gemma":[0.0001994852,0.0004347819,0.01135838,0.0002199691,0.0001150336,0.001115759,0.0004267924,0.5621325,0.02243316,0.05646172,0.3448739,0.0002285347],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03298951,0.001119319,0.8154289,0.0009124547,0.0005283179,0.0003770065,0.01521682,0.1010537,0.03237404],"genre_scores_gemma":[0.4903144,0.001318644,0.4124936,0.001632664,0.0003562226,0.0006665722,0.03511783,0.006711275,0.05138883],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01737112,"threshold_uncertainty_score":0,"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."}}