{"id":"W4313266805","doi":"10.1145/3567600.3568158","title":"Underwater Depth Calibration Using a Commercial Depth Camera","year":2022,"lang":"en","type":"article","venue":"","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Science Foundation","keywords":"Underwater; Calibration; Measured depth; Computer science; Remote sensing; Camera resectioning; Artificial intelligence; Computer vision; Environmental science; Geology; Mathematics","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":[0.001092331,0.0008523522,0.0005255633,0.001192448,0.0005284147,0.0006042214,0.001375262,0.0009813416,0.006695359],"category_scores_gemma":[0.003957903,0.0004744012,0.0004603458,0.001053704,0.0004629229,0.001217638,0.00141097,0.000794858,0.001322235],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007163563,"about_ca_system_score_gemma":0.0008735042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003721788,"about_ca_topic_score_gemma":0.005064749,"domain_scores_codex":[0.998133,0.0001138248,0.00009595892,0.0004976684,0.001043156,0.0001164399],"domain_scores_gemma":[0.998166,0.0002848227,0.000222122,0.000296853,0.0009634288,0.00006665344],"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":[0.0004313097,0.0002977842,0.01428062,0.0008912546,0.00006733194,0.0003723904,0.0005828224,0.007215415,0.6757447,0.002366532,0.006536684,0.2912132],"study_design_scores_gemma":[0.00018056,0.001278528,0.04366437,0.0002202366,0.000189673,0.002162698,0.0004895541,0.1064755,0.7928767,0.00117861,0.05102652,0.0002570993],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1921514,0.0007024658,0.7881431,0.0002915244,0.000303402,0.001453815,0.0009829035,0.003999691,0.01197163],"genre_scores_gemma":[0.3416372,0.0006022664,0.6502897,0.0003600738,0.00005391208,0.0008801132,0.0006672371,0.0004278687,0.005081623],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006695359,"threshold_uncertainty_score":0.02239823,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0680202530234454,"score_gpt":0.2870004414918443,"score_spread":0.2189801884683989,"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."}}