{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002618975,0.000223638,0.0002818898,0.0001557707,0.0003824816,0.0001548939,0.002176524,0.0002147737,0.000006297852],"category_scores_gemma":[0.003174118,0.0001552913,0.00007434603,0.0001126632,0.0004971744,0.0003454746,0.001047977,0.0004173084,0.000006111974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005494767,"about_ca_system_score_gemma":0.000006659926,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001596373,"about_ca_topic_score_gemma":0.00000402877,"domain_scores_codex":[0.9990963,0.000004853726,0.000217633,0.0002462945,0.0001585108,0.0002763984],"domain_scores_gemma":[0.9987324,0.00009624123,0.0001975174,0.0008249996,0.000132256,0.00001653104],"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":[0.0002796002,0.0002307795,0.07682992,0.001017007,0.0004360159,0.00000500121,0.001574642,0.000944381,0.3181787,0.01643969,0.02407642,0.5599879],"study_design_scores_gemma":[0.0002220983,0.0002534389,0.001202581,0.0004164953,0.0000174617,0.000007543093,0.004764353,0.001735203,0.9636223,0.02466136,0.002870273,0.0002269437],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9859588,0.00046552,0.0000881801,0.0009373846,0.0002673307,0.0004697012,0.000008097819,0.001735389,0.01006961],"genre_scores_gemma":[0.9984168,0.0006152397,0.0004807466,0.00002994289,0.00002060438,0.0001779724,2.218485e-7,0.00002977733,0.0002286613],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6454436,"threshold_uncertainty_score":0.6332597,"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."}}