{"id":"W4224124167","doi":"10.3410/f.738775355.793592558","title":"Faculty Opinions recommendation of Ultrastructural analysis of SARS-CoV-2 interactions with the host cell via high resolution scanning electron microscopy.","year":2022,"lang":"en","type":"dataset","venue":"Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature","topic":"Dermatological and COVID-19 studies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Ultrastructure; Resolution (logic); Scanning electron microscope; Host (biology); Electron microscope; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Coronavirus disease 2019 (COVID-19); High resolution; Materials science; Chemistry; Biology; Computer science; Physics; Medicine; Optics; Pathology; Artificial intelligence; Genetics; Remote sensing; Geography; Composite material","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007308545,0.0004935071,0.001434636,0.0006030383,0.0004549952,0.00005662881,0.0009457351,0.0002936663,0.0007978605],"category_scores_gemma":[0.001457018,0.0002523702,0.000826218,0.004898325,0.0005692308,0.0002025158,0.0003922024,0.001346753,0.000004656726],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002054361,"about_ca_system_score_gemma":0.0003659695,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003016402,"about_ca_topic_score_gemma":0.00004214926,"domain_scores_codex":[0.9957247,0.0005256811,0.001447709,0.0006137094,0.001328931,0.0003593155],"domain_scores_gemma":[0.9928774,0.0002710891,0.002081373,0.001142172,0.003510605,0.0001173798],"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":[0.00009995681,0.000381044,0.00004048113,0.003336931,0.001170055,4.608281e-7,0.0001869092,0.000001130799,0.0003616232,0.00004527661,0.9941455,0.0002306889],"study_design_scores_gemma":[0.0006357983,0.0003374508,0.008745337,0.002419287,0.00282646,0.00005968685,0.0001232745,0.00002261059,0.0006696279,0.000005271093,0.9839195,0.0002356703],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007439053,0.000910483,0.00009239936,0.17558,0.0003191041,0.001040399,0.8219241,0.00002626157,0.00003280355],"genre_scores_gemma":[0.000957883,0.0002800822,0.0003766599,0.005245598,0.00008904874,0.0002459828,0.992485,0.00001928931,0.0003004179],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1705609,"threshold_uncertainty_score":0.9999928,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0301885012183645,"score_gpt":0.3718654637665342,"score_spread":0.3416769625481696,"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."}}