{"id":"W1881529840","doi":"10.1109/cscwd.2015.7230981","title":"Indoor location based on WiFi","year":2015,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Computer science; Position (finance); Scheme (mathematics); Track (disk drive); Set (abstract data type); Real-time computing; Calibration; Location data; Computer network; Statistics; Mathematics","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":[0.0002915582,0.001076347,0.0007857891,0.001288962,0.0008715438,0.001282404,0.001284251,0.001060963,0.003172346],"category_scores_gemma":[0.001385047,0.0002775653,0.0008279785,0.002234837,0.0004982487,0.00205896,0.001687149,0.0006149887,0.002321221],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004879483,"about_ca_system_score_gemma":0.0005837991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006177604,"about_ca_topic_score_gemma":0.006125771,"domain_scores_codex":[0.9985369,0.0003755014,0.00006549132,0.0003284687,0.0003886679,0.0003050099],"domain_scores_gemma":[0.9995229,0.00008391018,0.00007259301,0.0001500656,0.0001400886,0.00003044345],"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":[0.000579102,0.0001621707,0.02176614,0.0009327768,0.000336805,0.002683938,0.0005127815,0.289346,0.03259002,0.07039231,0.01849706,0.5622008],"study_design_scores_gemma":[0.00006119098,0.0005682628,0.0121383,0.0001918104,0.0004163757,0.005227043,0.0006358551,0.8725405,0.03206533,0.01636851,0.05953853,0.0002483243],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03870889,0.001951391,0.9315656,0.000274515,0.0004862908,0.0001223712,0.0009072419,0.002269124,0.02371454],"genre_scores_gemma":[0.8717237,0.001791693,0.1151976,0.0001465644,0.0003434629,0.0001118098,0.001269431,0.00007861348,0.009337185],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006177604,"threshold_uncertainty_score":0.01228327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01889674160591472,"score_gpt":0.211493634915475,"score_spread":0.1925968933095603,"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."}}