{"id":"W4412885759","doi":"10.1088/1361-6501/adf134","title":"Development and testing of calibration device for differential pressure hydrostatic leveling based on liquid-level rise and fall method","year":2025,"lang":"en","type":"article","venue":"Measurement Science and Technology","topic":"Flow Measurement and Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Transportation of Ontario","funders":"","keywords":"Calibration; Differential pressure; Hydrostatic equilibrium; Differential (mechanical device); Hydrostatic pressure; Mechanics; Materials science; Hydrostatic test; Environmental science; Computer science; Geology; Mathematics; Physics; Thermodynamics; Composite material; Statistics","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":[],"consensus_categories":[],"category_scores_codex":[0.001378597,0.000128695,0.0001946285,0.0005422139,0.0002062069,0.00003878067,0.0001136786,0.00006885015,8.102165e-7],"category_scores_gemma":[0.0006358296,0.0001154665,0.00001109344,0.000694556,0.0001406787,0.00009257715,0.00004206158,0.00007654868,7.257608e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005220067,"about_ca_system_score_gemma":0.0001284779,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009467695,"about_ca_topic_score_gemma":0.00005365725,"domain_scores_codex":[0.9988463,0.00001392649,0.0002366812,0.0002722589,0.0004266867,0.0002041311],"domain_scores_gemma":[0.9994017,0.0000573114,0.00004544322,0.0001182964,0.0003410618,0.0000361463],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002833934,0.00004404308,0.004079286,0.0005848668,0.00007814148,4.005246e-7,0.0001644892,0.002498253,0.8876398,0.0007847396,0.00003054423,0.1040671],"study_design_scores_gemma":[0.0004678786,0.00007569633,0.001059335,0.0001944963,0.00009055332,3.584364e-7,0.0000888939,0.5931811,0.4039608,0.0002099525,0.0005468603,0.0001241041],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6515312,0.0007736994,0.3467112,0.000340467,0.00006626455,0.0003323211,0.000004284863,0.0001120993,0.0001284867],"genre_scores_gemma":[0.9495912,0.000007156455,0.05031998,0.00002076832,0.000004904366,0.00004068906,6.535458e-7,0.000006123873,0.000008538284],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5906829,"threshold_uncertainty_score":0.4708588,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05999265411983287,"score_gpt":0.2671453090292328,"score_spread":0.2071526549094,"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."}}