{"id":"W2117148943","doi":"10.1109/joe.2003.819310","title":"Quantitative visualization of geophysical flows using low-cost oblique digital time-lapse imaging","year":2003,"lang":"en","type":"article","venue":"IEEE Journal of Oceanic Engineering","topic":"Coastal and Marine Dynamics","field":"Earth and Planetary Sciences","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Aliasing; Visualization; Oblique case; Remote sensing; Computer science; Sampling (signal processing); Geology; Scale (ratio); Flow (mathematics); Internal wave; Flow visualization; Geophysics; Meteorology; Computer vision; Geography; Artificial intelligence; Filter (signal processing); Physics; Cartography; Oceanography","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":[0.0001894833,0.000115068,0.0002114549,0.0001432299,0.00002775222,0.00004574613,0.0001007204,0.00002559275,0.00005510253],"category_scores_gemma":[0.000139627,0.0001018397,0.00009576267,0.0002274766,0.00001723874,0.0005804524,0.000006510702,0.0001332709,0.000007337427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001211429,"about_ca_system_score_gemma":0.00006803339,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002420794,"about_ca_topic_score_gemma":0.000005059486,"domain_scores_codex":[0.9991768,0.00001951236,0.0003485926,0.00008202263,0.0002026395,0.0001704117],"domain_scores_gemma":[0.9994541,0.00009309706,0.0001820142,0.00006126233,0.0001202408,0.00008925654],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009195993,0.0000872993,0.03276313,0.0001295589,0.00009144619,0.0001075811,0.0002788789,0.9379256,0.02003314,0.001081205,0.00005654333,0.007353686],"study_design_scores_gemma":[0.0002637666,0.00008283028,0.007034911,0.000119349,0.00002233804,0.0001742813,0.00004274336,0.9907284,0.001087282,0.0001405176,0.0001632359,0.0001403421],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9114224,0.0000894456,0.08757057,0.000006417769,0.0003855005,0.00005299089,0.00002018525,0.00001088197,0.0004416406],"genre_scores_gemma":[0.997414,0.0000148153,0.002443527,0.000007623834,0.000078842,3.573169e-8,0.000007603448,0.000007279883,0.00002628319],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08599162,"threshold_uncertainty_score":0.4152901,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00781213006068854,"score_gpt":0.2186301857761763,"score_spread":0.2108180557154878,"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."}}