{"id":"W2582371371","doi":"","title":"Combining CT scan and particle imaging techniques: applications in geosciences.","year":2016,"lang":"en","type":"article","venue":"EspaceINRS (National Institute for Scientific Research (Canada))","topic":"Hydrology and Sediment Transport Processes","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministère des Transports","keywords":"Geology; Scanner; Sediment transport; Particle image velocimetry; Data acquisition; Image resolution; Velocimetry; Temporal resolution; Particle (ecology); Fluid dynamics; Sediment; Computer science; Geomorphology; Mechanics; Physics; Optics; Turbulence; Computer vision; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006849562,0.0005104919,0.0002828085,0.001794023,0.0002731218,0.001072442,0.0006440249,0.0009616755,0.0055429],"category_scores_gemma":[0.001219646,0.0004563757,0.0002981662,0.002558845,0.0006143001,0.0007175714,0.001138834,0.0004116446,0.001095638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005017883,"about_ca_system_score_gemma":0.0009131739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003257437,"about_ca_topic_score_gemma":0.005309155,"domain_scores_codex":[0.9997975,0.00005271788,0.00001349403,0.0000390184,0.00008563463,0.00001159622],"domain_scores_gemma":[0.999459,0.0002334798,0.00005464765,0.0000868701,0.00011127,0.00005477136],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002940792,0.0001396576,0.02322767,0.0009573045,0.0001581542,0.0008976716,0.0002592172,0.07073196,0.1098108,0.01975964,0.009044796,0.7647191],"study_design_scores_gemma":[0.0000954994,0.0002998945,0.02746539,0.0003423248,0.0002122871,0.002430461,0.0004971679,0.7290219,0.09270375,0.03411006,0.1125994,0.0002218292],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02694932,0.003473031,0.9545236,0.0006223312,0.0001312176,0.0001846721,0.0009842181,0.00186405,0.01126757],"genre_scores_gemma":[0.2297803,0.004818342,0.7572412,0.0002205855,0.0001434384,0.0002704508,0.0009692385,0.0002981611,0.006258272],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0055429,"threshold_uncertainty_score":0.01854289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02704490009422048,"score_gpt":0.300430035189434,"score_spread":0.2733851350952134,"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."}}