{"id":"W3091860506","doi":"10.1016/j.joen.2020.08.027","title":"Optimizing Methods for Bovine Dental Pulp Decellularization","year":2020,"lang":"en","type":"article","venue":"Journal of Endodontics","topic":"Tissue Engineering and Regenerative Medicine","field":"Medicine","cited_by":46,"is_retracted":false,"has_abstract":false,"ca_institutions":"Mount Sinai Hospital; University of Toronto","funders":"National Institute for Medical Research Development","keywords":"Decellularization; In vivo; Pulp (tooth); Chemistry; Trypsin; Sodium dodecyl sulfate; Extracellular matrix; Chromatography; Medicine; Biology; Biochemistry; Pathology; Enzyme; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003071584,0.00009204172,0.0003300618,0.00007519597,0.00003058289,0.00001037175,0.00005678365,0.00004891611,0.000018943],"category_scores_gemma":[0.0009212604,0.00006714553,0.0001265091,0.0001250677,0.00002118227,0.00004887963,0.0000115278,0.0001944921,0.000001045692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003439541,"about_ca_system_score_gemma":0.00005204288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":3.68161e-7,"about_ca_topic_score_gemma":1.168418e-7,"domain_scores_codex":[0.9992874,0.00003102702,0.0003444064,0.00007268354,0.0001453549,0.0001191294],"domain_scores_gemma":[0.9992573,0.00009933441,0.0001678656,0.00005826347,0.0002121375,0.0002051309],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005076174,0.0001115984,0.001826128,0.0002919654,0.0003262106,0.0001528186,0.001840769,0.003305411,0.9613166,0.0006744797,0.003357823,0.02628854],"study_design_scores_gemma":[0.01081229,0.006801759,0.0007336124,0.000490016,0.001089182,0.001537094,0.001148255,0.1667784,0.5197285,0.00005536562,0.2904979,0.0003276333],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02530598,0.007850193,0.962642,0.003402002,0.0006034914,0.0001330189,0.000001410353,0.00001564592,0.00004627843],"genre_scores_gemma":[0.2878813,0.0001311122,0.7094648,0.0002036558,0.001706753,0.000001215243,0.000007704723,0.00002519387,0.000578329],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4415881,"threshold_uncertainty_score":0.2738115,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04529131785206107,"score_gpt":0.3556050443121189,"score_spread":0.3103137264600578,"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."}}