{"id":"W4285451113","doi":"10.32920/ryerson.14653020","title":"Mapping underground infrastructure using photogrammetric methods","year":2021,"lang":"en","type":"preprint","venue":"","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Photogrammetry; Mobile mapping; Computer science; Global Positioning System; Digital mapping; Data collection; Remote sensing; Real-time computing; Geography; Telecommunications; 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":[0.0002788292,0.0006429949,0.0002842216,0.003901118,0.0004317517,0.001633864,0.0005891543,0.0005657756,0.003655203],"category_scores_gemma":[0.0009483158,0.0004165207,0.0003816765,0.003302461,0.0004711741,0.001072734,0.0009362707,0.0003525776,0.002196999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005309747,"about_ca_system_score_gemma":0.0006259787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006187927,"about_ca_topic_score_gemma":0.01025101,"domain_scores_codex":[0.9993696,0.00009920311,0.00002209501,0.0001200612,0.0003433502,0.00004576423],"domain_scores_gemma":[0.9996924,0.00006295943,0.00006158743,0.00008308671,0.0000877795,0.00001205603],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005360303,0.00009670074,0.01587145,0.0003734599,0.00007603698,0.0002238887,0.0006779119,0.1173295,0.05221976,0.008211047,0.004638921,0.8002277],"study_design_scores_gemma":[0.00005201415,0.0001947353,0.06243834,0.0002745808,0.00009762759,0.000914035,0.002205123,0.7931296,0.05769069,0.01891683,0.06388141,0.0002050106],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09757548,0.0005945546,0.8687,0.0002144904,0.00006540396,0.0002094201,0.001126958,0.00267982,0.02883381],"genre_scores_gemma":[0.5731302,0.001116127,0.4163899,0.00005592516,0.00003840731,0.0001948999,0.00132422,0.0001831915,0.007567065],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006187927,"threshold_uncertainty_score":0.01230377,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.074739201240105,"score_gpt":0.3160315070975578,"score_spread":0.2412923058574528,"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."}}