{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002647431,0.0003792368,0.0003614885,0.0008114683,0.0001021625,0.00007212793,0.0006242973,0.0004325594,0.00004506889],"category_scores_gemma":[0.00007453794,0.0004867646,0.000336179,0.001263571,0.00007304625,0.00006249289,0.0005067541,0.0007840594,0.0002045225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005911969,"about_ca_system_score_gemma":0.0001816893,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001145488,"about_ca_topic_score_gemma":0.00009420922,"domain_scores_codex":[0.9983122,0.00002539378,0.0002931829,0.0009003185,0.00007824897,0.0003905923],"domain_scores_gemma":[0.9983698,0.0002005453,0.00008282007,0.001017024,0.0001651341,0.0001647417],"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":[0.00001240408,0.0000372826,0.0004181778,0.0009927859,0.0001145202,0.00001262618,0.00005605341,0.994096,0.00006252412,0.002185501,0.001407333,0.0006047327],"study_design_scores_gemma":[0.0003395648,0.00001855087,0.0002219781,0.0001339448,0.0001610183,3.509967e-7,0.00003347381,0.9623558,0.0002712934,0.03455776,0.001442988,0.0004632742],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.361905,0.000097355,0.6344209,0.00004571387,0.0006401742,0.0008089342,0.0002302311,0.0009240877,0.0009275216],"genre_scores_gemma":[0.9926813,0.00004310631,0.006427647,0.00004799727,0.0001512806,0.00002881329,0.0001569179,0.00008700318,0.0003759006],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6307763,"threshold_uncertainty_score":0.9997584,"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."}}