{"id":"W2732107624","doi":"10.1145/3090094","title":"Gain Without Pain","year":2017,"lang":"en","type":"article","venue":"Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":100,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bell (Canada)","funders":"Cisco Systems","keywords":"RSS; Fingerprint (computing); Computer science; Matching (statistics); Construct (python library); Ambiguity; Percentile; Data mining; Real-time computing; Artificial intelligence; Computer network; Statistics; Mathematics; 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":[0.0007378489,0.00197731,0.001452679,0.001325407,0.001086234,0.003131651,0.00226629,0.002169744,0.08636271],"category_scores_gemma":[0.004591453,0.0004717984,0.0008036252,0.001272245,0.0006766649,0.003887659,0.004377263,0.001921718,0.05400353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007754561,"about_ca_system_score_gemma":0.001004008,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001401625,"about_ca_topic_score_gemma":0.00191082,"domain_scores_codex":[0.9983869,0.0001443873,0.0000648343,0.0003181271,0.0007951725,0.0002906279],"domain_scores_gemma":[0.9984404,0.0003281214,0.00008588726,0.00047725,0.0005328075,0.0001354659],"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":[0.0006515021,0.0001882779,0.001198627,0.0004078489,0.00005699878,0.0004685494,0.0001497567,0.006302289,0.03769097,0.03270032,0.05862468,0.8615602],"study_design_scores_gemma":[0.000435734,0.00214852,0.005535221,0.0004601649,0.0004091527,0.009840702,0.0008615117,0.1695181,0.06266617,0.08883756,0.6589569,0.0003302409],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0318683,0.005924278,0.6672156,0.006622578,0.003014426,0.0004115844,0.001147215,0.02485893,0.2589371],"genre_scores_gemma":[0.5710361,0.004652303,0.2273902,0.00612473,0.001873562,0.0004785587,0.002384786,0.001532609,0.1845272],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.08636271,"threshold_uncertainty_score":0.2889121,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009640496580766779,"score_gpt":0.2410420920216475,"score_spread":0.2314015954408808,"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."}}