{"id":"W4248342484","doi":"10.32920/ryerson.14653467.v1","title":"An enhanced system for augmenting urban search and rescue canines","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Human-Animal Interaction Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Federal Emergency Management Agency","keywords":"Urban search and rescue; Search and rescue; Emergency rescue; Situation awareness; Computer science; Wearable computer; Computer security; Plan (archaeology); Medical emergency; Engineering; Medicine; Artificial intelligence; Robot; Geography; Embedded system","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":[0.0003343232,0.0003126569,0.0003976606,0.0004139758,0.0002251221,0.0004781452,0.0005851957,0.000656223,0.00639778],"category_scores_gemma":[0.0006034019,0.0001663389,0.000194533,0.0002795335,0.0001961524,0.0007597487,0.0008099075,0.000280425,0.001478028],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001896116,"about_ca_system_score_gemma":0.0002830949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009672969,"about_ca_topic_score_gemma":0.0009129242,"domain_scores_codex":[0.9998081,0.00002863749,0.00001040631,0.00006342124,0.00006539957,0.00002401274],"domain_scores_gemma":[0.9996855,0.0000735567,0.0000220444,0.00008223383,0.00009371671,0.0000429683],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002105221,0.0004954069,0.009486081,0.000504914,0.00009367346,0.001229244,0.0008939019,0.00873697,0.6425307,0.003805084,0.02380738,0.3063113],"study_design_scores_gemma":[0.0004816217,0.004414823,0.04342758,0.0001380737,0.0003942888,0.005705188,0.0006447964,0.469829,0.264712,0.002659935,0.2073641,0.0002285412],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5449753,0.0007116075,0.3906512,0.0005035328,0.0004473216,0.0008775999,0.002231176,0.03635498,0.02324728],"genre_scores_gemma":[0.7796944,0.000292889,0.199023,0.0003355184,0.0001163086,0.0003285213,0.001954076,0.0002623147,0.01799291],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00639778,"threshold_uncertainty_score":0.02140272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0317507002109634,"score_gpt":0.3746396099633152,"score_spread":0.3428889097523518,"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."}}