{"id":"W4244451909","doi":"10.3410/f.726889766.793530447","title":"Faculty Opinions recommendation of Increased spatiotemporal resolution reveals highly dynamic dense tubular matrices in the peripheral ER.","year":2017,"lang":"en","type":"dataset","venue":"Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Resolution (logic); Peripheral; Computer science; High resolution; Geology; Artificial intelligence; Remote sensing","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":[0.000870311,0.00191598,0.001246716,0.002141323,0.0006755937,0.002566708,0.001984342,0.002100847,0.02253289],"category_scores_gemma":[0.003845548,0.0004785248,0.00150635,0.002320522,0.0004476287,0.001346601,0.001756747,0.001311953,0.03235824],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009712305,"about_ca_system_score_gemma":0.00150572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01962044,"about_ca_topic_score_gemma":0.06543049,"domain_scores_codex":[0.9992717,0.00008192065,0.00004564373,0.0002650432,0.0002105981,0.0001250851],"domain_scores_gemma":[0.9987978,0.0002301112,0.0001176721,0.0003497898,0.0003551282,0.0001495349],"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":[0.0002043712,0.00006237767,0.004212197,0.0007154758,0.00008689404,0.0001160828,0.00003594128,0.0008962928,0.001502049,0.0006469002,0.9783281,0.01319326],"study_design_scores_gemma":[0.0003693337,0.00008850594,0.02911293,0.0004608355,0.0002060698,0.0009772719,0.0002432615,0.01401827,0.007009093,0.006160906,0.9412618,0.00009174445],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.008721033,0.001735033,0.002631076,0.001166217,0.0004539812,0.00006861743,0.9711279,0.006858983,0.007237061],"genre_scores_gemma":[0.01221726,0.0005957327,0.003709717,0.0002704899,0.00009285878,0.00005801375,0.9773502,0.0003525882,0.005352983],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02253289,"threshold_uncertainty_score":0.07537997,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02274404251011592,"score_gpt":0.3586680100497984,"score_spread":0.3359239675396825,"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."}}