{"id":"W4253133445","doi":"10.3410/f.736520474.793565588","title":"Faculty Opinions recommendation of Using MRI to measure in vivo free radical production and perfusion dynamics in a mouse model of elevated oxidative stress and neurogenic atrophy.","year":2019,"lang":"en","type":"dataset","venue":"Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature","topic":"Adipose Tissue and Metabolism","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"In vivo; Oxidative stress; Atrophy; Perfusion; Measure (data warehouse); Dynamics (music); Medicine; Pathology; Chemistry; Neuroscience; Internal medicine; Psychology; Biology; Computer science; Data mining; Biotechnology","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":[0.002104918,0.00248044,0.00151668,0.0024981,0.0006998579,0.002570974,0.002169766,0.002923908,0.03466725],"category_scores_gemma":[0.009302152,0.0005889499,0.002209788,0.002314592,0.0003840031,0.001129942,0.001694585,0.001477218,0.04758666],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001211455,"about_ca_system_score_gemma":0.002149851,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01798947,"about_ca_topic_score_gemma":0.04778432,"domain_scores_codex":[0.9988201,0.0002357582,0.0001222659,0.000417662,0.000272911,0.000131253],"domain_scores_gemma":[0.9971098,0.0008201621,0.0003400569,0.0006948694,0.0006880903,0.0003469299],"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":[0.0002988613,0.00006489249,0.003493672,0.001783101,0.0002031697,0.00004337014,0.00001490461,0.0003888134,0.0006498251,0.0001978318,0.9816612,0.01120036],"study_design_scores_gemma":[0.001014745,0.0001375869,0.02221007,0.001197639,0.0005418769,0.0002792202,0.00006731042,0.003267359,0.002771898,0.002407398,0.965996,0.000108882],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0008432968,0.0006471394,0.0003932962,0.0004101332,0.000168565,0.00005372324,0.9946948,0.001212994,0.001576039],"genre_scores_gemma":[0.00225015,0.0003511042,0.001342374,0.0002847994,0.00004858849,0.0001585516,0.9937249,0.0001265325,0.0017129],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03466725,"threshold_uncertainty_score":0.1159735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04185206774825421,"score_gpt":0.3395435456472765,"score_spread":0.2976914778990222,"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."}}