{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0004964026,0.0006296938,0.0005938932,0.0004569982,0.009871803,0.0002040984,0.000626703,0.0003902228,0.00002820907],"category_scores_gemma":[0.0004368548,0.0008041254,0.0003650521,0.0005304888,0.0001664303,0.0004787525,0.0003191765,0.0005095355,0.000002692728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002247851,"about_ca_system_score_gemma":0.001959298,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006682527,"about_ca_topic_score_gemma":0.0003919566,"domain_scores_codex":[0.9967573,0.0001777922,0.0005084396,0.001160689,0.0006446605,0.0007511508],"domain_scores_gemma":[0.997238,0.001092013,0.0005879549,0.000487867,0.0002937229,0.00030042],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005927878,0.0001380137,0.001093084,0.0005774935,0.0002178221,0.0004513095,0.009677077,0.8039945,0.1428778,0.0310736,0.00001306446,0.009293483],"study_design_scores_gemma":[0.001923725,0.0002618914,0.001818873,0.0003780581,0.0003123366,0.0001232034,0.003534919,0.8448374,0.1377156,0.0001333076,0.008192318,0.0007683426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3659053,0.005150677,0.6235158,0.0005524005,0.002064817,0.00174786,0.0001048602,0.0002439081,0.0007143241],"genre_scores_gemma":[0.9221903,0.00001472479,0.07452893,0.0001338298,0.0002586967,0.0002420319,0.0004385086,0.00008386275,0.002109075],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.556285,"threshold_uncertainty_score":0.9999321,"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."}}