{"id":"W4404903811","doi":"10.61618/ssov3472","title":"Voice Calling Detection Distance in Land Search and Rescue","year":2024,"lang":"en","type":"article","venue":"The Journal of Search and Rescue","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Search and rescue; Computer science; Speech recognition; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0006138559,0.0002575242,0.0002706115,0.0008434151,0.0002619492,0.0004729674,0.0003805613,0.0005155246,0.0009970047],"category_scores_gemma":[0.004077656,0.0001976036,0.000167812,0.000338759,0.0004531565,0.0005882944,0.000508476,0.0002779355,0.0004177916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003929857,"about_ca_system_score_gemma":0.0002111128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005349095,"about_ca_topic_score_gemma":0.007665385,"domain_scores_codex":[0.9995067,0.0001377714,0.00002645889,0.0001052784,0.0001831704,0.00004067798],"domain_scores_gemma":[0.9981455,0.001092451,0.0002587787,0.00006900579,0.0002721876,0.0001620879],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001506798,0.000259784,0.7080722,0.0003487241,0.0001225201,0.001437132,0.005342439,0.04375307,0.09603617,0.001000382,0.0006222391,0.1414984],"study_design_scores_gemma":[0.00002196154,0.0005595225,0.9356734,0.00004785117,0.00004572204,0.001729299,0.002224061,0.04435444,0.01364691,0.0007651472,0.0008580053,0.00007372112],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9923961,0.0002922647,0.005558778,0.0000272963,0.00000440513,0.000008200096,0.000049785,0.00004306943,0.001620046],"genre_scores_gemma":[0.998021,0.0000430713,0.001406427,0.000009929875,0.000002514684,0.000006360317,0.00004874645,0.000008320152,0.0004536876],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005349095,"threshold_uncertainty_score":0.01063591,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02945089818660091,"score_gpt":0.2843155018237495,"score_spread":0.2548646036371486,"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."}}