{"id":"W4406022276","doi":"10.48550/arxiv.2408.07757","title":"Inverse k-visibility for RSSI-based Indoor Geometric Mapping","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"3D Modeling in Geospatial Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Visibility; Inverse; Computer science; Geography; Computer vision; Geodesy; Artificial intelligence; Remote sensing; Mathematics; Geometry; Meteorology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004801606,0.000766464,0.0006937244,0.001211778,0.0004651255,0.00130659,0.001185148,0.0006133395,0.00101905],"category_scores_gemma":[0.004149588,0.0004560976,0.0009078251,0.001535454,0.0009797693,0.001434938,0.002569528,0.001141954,0.0007132986],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004529907,"about_ca_system_score_gemma":0.0009706846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003164435,"about_ca_topic_score_gemma":0.003158357,"domain_scores_codex":[0.9992473,0.0001625413,0.00003239443,0.0001545354,0.0003179067,0.00008531117],"domain_scores_gemma":[0.9990435,0.0003465305,0.0001369344,0.0002437178,0.0001878065,0.00004147525],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002351439,0.00008651402,0.003576726,0.0003014816,0.0001060695,0.000256904,0.0006370904,0.4241446,0.02242182,0.06423627,0.002723913,0.4812734],"study_design_scores_gemma":[0.00001209338,0.00005944743,0.0009760704,0.00003120616,0.00001489615,0.00025952,0.00009790229,0.9546227,0.01104022,0.02701221,0.005836148,0.00003752486],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007253713,0.0001062839,0.9910715,0.00003996774,0.00002323117,0.00000904203,0.00003766227,0.0004041506,0.001054515],"genre_scores_gemma":[0.4994016,0.0003810308,0.4979152,0.0000448787,0.0000482719,0.00007489423,0.0003822585,0.0002119104,0.001539833],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003164435,"threshold_uncertainty_score":0.006292045,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08614779866798226,"score_gpt":0.1919414185930877,"score_spread":0.1057936199251054,"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."}}