{"id":"W3041469537","doi":"10.1007/s00371-020-01889-3","title":"A density-accurate tracking solution for smoke upresolution","year":2020,"lang":"en","type":"article","venue":"The Visual Computer","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Animation; Computation; Tracking (education); Computer graphics; Smoke; Field (mathematics); BitTorrent tracker; Low resolution; Computer vision; Resolution (logic); Computer animation; High resolution; Artificial intelligence; Computer graphics (images); Algorithm; Eye tracking; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003749271,0.0001762341,0.0001814327,0.00007943274,0.0003742969,0.0004183236,0.000864877,0.00006670347,0.000002730373],"category_scores_gemma":[0.00002081268,0.0001386127,0.0001456587,0.000510367,0.000046328,0.0004222642,0.0004945445,0.0001381427,0.00001055615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002259057,"about_ca_system_score_gemma":0.00003592255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001467583,"about_ca_topic_score_gemma":0.000002401403,"domain_scores_codex":[0.9986383,0.0001196772,0.0002908793,0.0004271662,0.0002284074,0.0002955526],"domain_scores_gemma":[0.9991077,0.0001414789,0.0001367009,0.0003111926,0.0002018374,0.0001011392],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006060492,0.0001484578,0.0003009583,0.00007841401,0.00007395708,0.000005785836,0.003520497,0.0003527822,0.002215284,0.7809854,0.01696656,0.1952914],"study_design_scores_gemma":[0.0002584681,0.0003700199,0.00140207,0.00001657866,0.000008824093,0.000006699385,0.000003105298,0.9825478,0.003332368,0.005035465,0.006826803,0.0001918509],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01353442,0.00005254351,0.9813549,0.003518312,0.0003746147,0.0004452877,0.000001990491,0.0006954053,0.00002255792],"genre_scores_gemma":[0.9532726,0.00001157913,0.04112543,0.004918204,0.0006080691,0.00003115394,0.000009483069,0.00001659716,0.000006867641],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.982195,"threshold_uncertainty_score":0.5652462,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07439559811669616,"score_gpt":0.3325882813615943,"score_spread":0.2581926832448981,"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."}}