{"id":"W2066532637","doi":"10.1016/s0142-9612(03)00334-x","title":"Fiber templating of poly(2-hydroxyethyl methacrylate) for neural tissue engineering","year":2003,"lang":"en","type":"article","venue":"Biomaterials","topic":"Electrospun Nanofibers in Biomedical Applications","field":"Materials Science","cited_by":188,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Polycaprolactone; Materials science; Neural tissue engineering; Methacrylate; 2-Hydroxyethyl Methacrylate; Self-healing hydrogels; Electrospinning; Tissue engineering; Composite material; Fiber; Biomedical engineering; Sonication; Composite number; Fabrication; Polymer; Chemical engineering; Polymerization; Polymer chemistry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0008325591,0.0001892849,0.0003675103,0.00009268255,0.0000973427,0.00005142769,0.0002628171,0.0001419694,0.001064888],"category_scores_gemma":[0.0002061554,0.000169302,0.00006529546,0.000233978,0.00009518157,0.0001059448,0.00004434206,0.000011764,0.0001066225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003865777,"about_ca_system_score_gemma":0.00003816137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003437861,"about_ca_topic_score_gemma":7.025979e-7,"domain_scores_codex":[0.9983575,0.00007133158,0.0005940592,0.0003321826,0.0002124217,0.000432462],"domain_scores_gemma":[0.9989837,0.0002728033,0.0002315834,0.0003326781,0.00007571306,0.0001034601],"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":[0.00001042997,0.00002941325,0.000009901614,0.00009326749,0.000009290537,6.265686e-7,0.00004820826,0.000008928795,0.9964963,0.002281256,0.0002558331,0.0007565927],"study_design_scores_gemma":[0.0002931762,0.00008916866,0.00005373252,0.00002416115,0.00002180663,0.0000121812,0.000005733512,0.00007766578,0.9826974,0.0006278314,0.01591216,0.0001850411],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.987972,0.0003982799,0.009449543,0.0001276194,0.0007839255,0.000672142,0.000139005,0.0001852255,0.000272312],"genre_scores_gemma":[0.8142069,0.000003307506,0.1851016,0.00003506571,0.0001407754,0.0001928113,0.00001810539,0.00003902195,0.0002624128],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1756521,"threshold_uncertainty_score":0.9998482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01525395609525281,"score_gpt":0.2770880711718215,"score_spread":0.2618341150765687,"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."}}