{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002322411,0.00009854644,0.00009773069,0.0001170587,0.0009736069,0.00009657464,0.0003942653,0.00001977633,0.0001684401],"category_scores_gemma":[0.0002594818,0.00007498496,0.00001637947,0.0008622706,0.001409333,0.0006591981,0.0001064647,0.0001170339,0.000006671694],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005795194,"about_ca_system_score_gemma":0.001172364,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1952123,"about_ca_topic_score_gemma":0.980262,"domain_scores_codex":[0.9975408,0.00004452413,0.0002135184,0.0005478826,0.001136426,0.0005168428],"domain_scores_gemma":[0.9992446,0.0002704592,0.00004325612,0.000170067,0.00009785051,0.0001737417],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006894431,0.0003631856,0.7456144,0.00007677772,0.00002190628,0.00005177061,0.000436064,0.002217736,0.04934427,0.1193387,0.03014769,0.05231856],"study_design_scores_gemma":[0.0004441604,0.00002395578,0.02952111,0.00004031066,0.000002857951,0.000005269158,0.0001611077,0.002609753,0.02007277,0.00621945,0.9406849,0.0002143266],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9346743,0.0001216121,0.006083769,0.050047,0.0004564542,0.001420612,0.0001803108,0.00005621595,0.006959687],"genre_scores_gemma":[0.9974118,0.000009862005,0.0006154524,0.0001231576,0.00002022053,0.0002927414,0.00002583596,0.000006029215,0.001494859],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9105372,"threshold_uncertainty_score":0.8101468,"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."}}