{"id":"W6945234175","doi":"10.25545/eaj1ci","title":"Quantification and Characterization of Turbulent Flow using Magnetic Resonance","year":2022,"lang":"en","type":"dataset","venue":"UNB Dataverse","topic":"Botanical Research and Applications","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Characterization (materials science); Turbulence; Flow (mathematics); Magnetic resonance imaging; MATLAB","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001670116,0.0001029841,0.0001401687,0.00001376269,0.0001601641,0.00003573157,0.0003069046,0.00008181936,0.01815496],"category_scores_gemma":[0.00006590547,0.00004888352,0.00003099159,0.0002627885,0.00007876518,0.00007708554,0.0002061461,0.0001641942,0.00003579517],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002373322,"about_ca_system_score_gemma":0.00001516833,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004202487,"about_ca_topic_score_gemma":0.0001392673,"domain_scores_codex":[0.9990075,0.00007262865,0.0002068053,0.0003034933,0.0002606031,0.0001489946],"domain_scores_gemma":[0.9995003,0.00008584666,0.0001261618,0.0001680877,0.00004472569,0.00007485017],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004045327,0.0001587892,0.00001865405,0.00004243356,0.000005397393,0.000004206392,0.000005190754,0.000001384346,0.19159,0.00005001006,0.7494479,0.05863557],"study_design_scores_gemma":[0.00004698914,0.00009104363,0.003916922,0.00001705352,0.00001625651,0.000003435822,0.00001316066,0.0004028698,0.0001419691,0.00002054508,0.9952318,0.00009797121],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0283502,0.00008815477,0.000002916808,0.000157739,0.0000321676,0.0002535145,0.9711046,0.000008442036,0.00000231004],"genre_scores_gemma":[0.0004726833,0.001628974,0.00005606734,0.00005737132,0.00006136495,0.00002373847,0.9976469,7.337359e-7,0.00005212767],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2457839,"threshold_uncertainty_score":0.9827426,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04282703044554052,"score_gpt":0.2661592462340625,"score_spread":0.2233322157885219,"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."}}