{"id":"W2291398142","doi":"10.1109/globalsip.2015.7418216","title":"RSS difference-aware graph-based semi-supervised learning (RG-SSL) RSS smoothing method for crowdsourcing indoor localization","year":2015,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"RSS; Crowdsourcing; Computer science; Signal strength; Smoothing; Graph; Exploit; Workload; Artificial intelligence; Data mining; Machine learning; Computer vision; Antenna (radio); Theoretical computer science","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":[0.001002438,0.0009682876,0.001340133,0.00103437,0.0006028655,0.0006377313,0.002766722,0.0009812579,0.001275111],"category_scores_gemma":[0.003689591,0.0004209458,0.0009142407,0.0009885408,0.0009310751,0.001113798,0.001515011,0.001015117,0.0007071893],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006938206,"about_ca_system_score_gemma":0.001146712,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005451118,"about_ca_topic_score_gemma":0.006300793,"domain_scores_codex":[0.9989257,0.0002943651,0.00005352557,0.0003088891,0.0003332973,0.00008421508],"domain_scores_gemma":[0.9978657,0.000801658,0.0002795137,0.0003746721,0.0005709741,0.0001074528],"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":[0.0004152731,0.0002692358,0.002497862,0.0003123597,0.0001406653,0.0002294861,0.0003623541,0.5316515,0.01689093,0.00472958,0.005206889,0.4372938],"study_design_scores_gemma":[0.00001135369,0.00003639573,0.0003329653,0.000005035601,0.000009642979,0.00003654567,0.00001920241,0.9946337,0.002375699,0.001933096,0.0005928374,0.00001355396],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01722201,0.0001211391,0.979931,0.0001075695,0.00004393745,0.0000506173,0.00006383747,0.001674427,0.0007855945],"genre_scores_gemma":[0.7560628,0.0001702024,0.2388938,0.0002601434,0.0001113839,0.0002115742,0.0005018113,0.0002850465,0.003503152],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005451118,"threshold_uncertainty_score":0.01083875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02540753424404589,"score_gpt":0.2575863876170597,"score_spread":0.2321788533730138,"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."}}