{"id":"W2027538694","doi":"10.1016/j.ijbiomac.2006.04.010","title":"Kinetic and mechanistic considerations in the gelation of genipin-crosslinked gelatin","year":2006,"lang":"en","type":"article","venue":"International Journal of Biological Macromolecules","topic":"Collagen: Extraction and Characterization","field":"Materials Science","cited_by":100,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University; Dalhousie University; University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Advanced Foods and Materials Network","keywords":"Genipin; Gelatin; Arrhenius equation; Arrhenius plot; Activation energy; Covalent bond; Chemistry; Hydrogen bond; Polymer chemistry; Kinetic energy; Chemical engineering; Materials science; Physical chemistry; Molecule; Organic chemistry; Chitosan","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008491243,0.0004808911,0.0002545014,0.0001903217,0.0002733,0.0006660454,0.0005003683,0.000498482,0.001944166],"category_scores_gemma":[0.001096916,0.0003186454,0.0003685309,0.0001173923,0.0005612168,0.001546752,0.0002488211,0.0008860666,0.0004029525],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001011844,"about_ca_system_score_gemma":0.0004542832,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001270303,"about_ca_topic_score_gemma":0.001574584,"domain_scores_codex":[0.9997826,0.0000536969,0.00001505314,0.0000465304,0.00003477489,0.00006733822],"domain_scores_gemma":[0.9994882,0.0003622073,0.0000682579,0.00002078828,0.0000306746,0.00002988053],"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":[0.001082433,0.0001515449,0.00127129,0.0003845609,0.0000354972,0.0004577961,0.0002176783,0.008907538,0.9734024,0.008165855,0.0002077572,0.005715574],"study_design_scores_gemma":[0.00003784402,0.0003272315,0.002940665,0.00003471269,0.00003403901,0.0002124879,0.0001302833,0.02154741,0.9712353,0.001807244,0.001656855,0.00003596316],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9732372,0.003406425,0.01857961,0.0005612526,0.00005980588,0.00007474916,0.0002199231,0.00004652395,0.003814494],"genre_scores_gemma":[0.9961837,0.0009347008,0.001849019,0.00003972798,0.000006944709,0.00002719931,0.0000760774,0.00001116249,0.0008715366],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001944166,"threshold_uncertainty_score":0.007341504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02072068812416463,"score_gpt":0.2699565927636985,"score_spread":0.2492359046395339,"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."}}