{"id":"W4311480772","doi":"10.21203/rs.3.rs-2356060/v1","title":"Large-scale volumetric particle tracking using a single camera: Analysis of the scalability and accuracy of glare-point particle tracking","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Flow Measurement and Analysis","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"California Institute of Technology","keywords":"Tracking (education); Bubble; Soap bubble; Scalability; Wind tunnel; Particle tracking velocimetry; Computer science; Optics; Particle (ecology); Generator (circuit theory); Scale (ratio); Artificial intelligence; Computer vision; Physics; Turbulence; Power (physics); Particle image velocimetry; Mechanics","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":[0.001409387,0.0004331124,0.000474857,0.0005773847,0.000223754,0.0005715305,0.000729962,0.0005902065,0.0004545516],"category_scores_gemma":[0.005007008,0.0002014622,0.0002714586,0.0004986388,0.0004990636,0.0007775935,0.0005083595,0.0004572146,0.0001461548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004880895,"about_ca_system_score_gemma":0.0003790086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004215452,"about_ca_topic_score_gemma":0.001950764,"domain_scores_codex":[0.9993606,0.00006932449,0.0000238911,0.0001542715,0.0003283064,0.00006356793],"domain_scores_gemma":[0.9974318,0.001287283,0.000347041,0.0003124767,0.0005280903,0.00009326621],"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":[0.0006821207,0.0002612334,0.02594226,0.0004076067,0.0001343534,0.0005022597,0.0003180341,0.2088396,0.6558864,0.001678003,0.0008993696,0.1044489],"study_design_scores_gemma":[0.00001730752,0.0002679737,0.01757506,0.0000140512,0.00002346361,0.0001743367,0.00005992774,0.87922,0.1016699,0.0004261288,0.0005169261,0.00003505068],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7833282,0.0006961468,0.2129314,0.000138861,0.00004834927,0.0001055601,0.0002424843,0.001133454,0.001375504],"genre_scores_gemma":[0.9665671,0.0001857741,0.03267936,0.00002444475,0.00001167152,0.00004628201,0.0002130252,0.00005336413,0.0002190021],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004215452,"threshold_uncertainty_score":0.008381844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09934158060897158,"score_gpt":0.3525242805656282,"score_spread":0.2531826999566566,"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."}}