{"id":"W7034353057","doi":"","title":"Thermosensitive chitosan-based hydrogels for extrusion-based bioprinting and injectable scaffold for articular tissue engineering","year":2022,"lang":"fr","type":"dissertation","venue":"Papyrus : Institutional Repository (Université de Montréal)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Self-healing hydrogels; Microsphere; Tissue engineering; Biocompatible material; Scaffold","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002226663,0.0003480319,0.0001636091,0.0002816761,0.0001192415,0.0002147276,0.0001829719,0.0003381955,0.001072985],"category_scores_gemma":[0.0001578113,0.0001597348,0.0002321676,0.0002236623,0.0001856232,0.0002819446,0.0001674787,0.0003273945,0.000226025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003026847,"about_ca_system_score_gemma":0.0003082037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007128539,"about_ca_topic_score_gemma":0.001703603,"domain_scores_codex":[0.9998642,0.0000115659,0.00001236925,0.00002576945,0.00006459167,0.00002139137],"domain_scores_gemma":[0.9998957,0.00002308243,0.00004018808,0.000007868664,0.00001944676,0.00001379922],"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":[0.00002880864,0.000009381131,0.00003314444,0.00005522612,0.000002061917,0.00002589566,0.000008068975,0.0001048949,0.9977526,0.00005316517,0.00002341183,0.001903448],"study_design_scores_gemma":[0.000008268732,0.0001109823,0.0007897393,0.0000057825,0.00001070739,0.00009518092,0.000006232531,0.0007175491,0.9967878,0.00001548292,0.001444125,0.000008140778],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9329212,0.0101162,0.05138742,0.0001898619,0.0001588724,0.0002272987,0.0003628328,0.0003327454,0.004303596],"genre_scores_gemma":[0.9532461,0.004434208,0.03430033,0.0001108042,0.00003130461,0.000131696,0.0002760029,0.00006777767,0.007401918],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001072985,"threshold_uncertainty_score":0.003589451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006522330521912679,"score_gpt":0.1948252232449244,"score_spread":0.1883028927230118,"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."}}