{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001803069,0.00009091981,0.00008314211,0.0000338302,0.0003567691,0.00003664511,0.0002874447,0.00003367263,0.00165361],"category_scores_gemma":[0.00001174221,0.00008313256,0.00003026162,0.0001877542,0.0001027739,0.0001906246,0.000915601,0.0001553412,0.00006290301],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003774085,"about_ca_system_score_gemma":0.000007074,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003764864,"about_ca_topic_score_gemma":0.000477174,"domain_scores_codex":[0.99902,0.00009703325,0.0001424303,0.000217166,0.0002996827,0.000223644],"domain_scores_gemma":[0.9996698,0.00002030554,0.00003836025,0.0002386433,0.000001575551,0.00003126279],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002254469,0.0001359382,0.9260065,0.000004548392,0.00001390693,0.00001822946,0.00104656,0.02006401,0.03868633,0.0005291965,0.006655354,0.006816882],"study_design_scores_gemma":[0.001359978,0.0004509646,0.4749148,0.000009541613,0.00006249187,0.0001162634,0.00556038,0.05722178,0.3963017,0.01183381,0.05054059,0.00162771],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.990961,0.000003267763,0.005225517,0.0008325399,0.0001919418,0.000123509,0.000002717387,0.000299043,0.002360449],"genre_scores_gemma":[0.9894211,5.386661e-7,0.009682701,0.0003102842,0.00002868706,0.00002038113,0.000006056857,0.00001253184,0.0005176613],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4510916,"threshold_uncertainty_score":0.999259,"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."}}