{"id":"W4411115628","doi":"10.2749/tokyo.2025.2009","title":"Post-disaster building indoor damage and survivor detection using autonomous path planning and deep learning with unmanned aerial vehicles","year":2025,"lang":"en","type":"article","venue":"Report","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Motion planning; Computer science; Deep learning; Aeronautics; Path (computing); Real-time computing; Environmental science; Artificial intelligence; Remote sensing; Engineering; Geography; Robot; Computer network","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":[0.0001533755,0.0001222216,0.0001481225,0.0001123393,0.0001479328,0.00009842753,0.00002107861,0.00007441017,0.00000130194],"category_scores_gemma":[0.00004266392,0.0001184559,0.00001376258,0.0001194547,0.00002515012,0.000115159,0.00002298575,0.0001266232,1.557048e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004607776,"about_ca_system_score_gemma":0.00001833756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008438624,"about_ca_topic_score_gemma":0.00002866493,"domain_scores_codex":[0.9993497,0.00002384285,0.0002054842,0.000185131,0.00008115153,0.0001547458],"domain_scores_gemma":[0.9997311,0.00003315974,0.00005653159,0.00009193577,0.00004555361,0.00004177143],"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":[0.00005072655,0.000007208952,0.06842265,0.0001328664,0.00005858283,0.0002472558,0.0004786856,0.7867828,0.1385752,0.00005365209,0.000001033,0.005189357],"study_design_scores_gemma":[0.0003886557,0.00005812419,0.03209132,0.0001386339,0.00004884785,0.0001698769,0.00033704,0.9577056,0.008660687,0.00002426512,0.0001850322,0.0001919564],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8470678,0.0001338743,0.1522872,0.000009042273,0.0001468466,0.00009358794,5.586639e-7,0.0001210965,0.0001399179],"genre_scores_gemma":[0.997578,0.000006855262,0.002269218,0.00001835081,0.00005321094,0.000002634564,0.000008441818,0.00002556283,0.00003776684],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1709228,"threshold_uncertainty_score":0.4830493,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006584639845233135,"score_gpt":0.2214148194586188,"score_spread":0.2148301796133857,"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."}}