{"id":"W2057507125","doi":"10.1002/mame.200700009","title":"Gel Point Investigation of a Chitosan System Using Fourier Transform Rheometry and Multi‐Frequency Excitation","year":2007,"lang":"en","type":"article","venue":"Macromolecular Materials and Engineering","topic":"Nanocomposite Films for Food Packaging","field":"Materials Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; University of Toronto","funders":"","keywords":"Rheometry; Chitosan; Materials science; Harmonics; Excitation; Fourier transform; Intensity (physics); Sine wave; Fourier transform infrared spectroscopy; Sine; Analytical Chemistry (journal); Optics; Physics; Viscoelasticity; Composite material; Chemical engineering; Chemistry; Chromatography; Mathematical analysis; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001875151,0.000256212,0.0001420995,0.0003603531,0.0001621936,0.0001707262,0.0002015208,0.0002476308,0.000883051],"category_scores_gemma":[0.0001929976,0.000110589,0.0001397946,0.0001808554,0.0002147503,0.0002668129,0.0001322329,0.000440296,0.0001541489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002442226,"about_ca_system_score_gemma":0.0001304021,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001110066,"about_ca_topic_score_gemma":0.00148493,"domain_scores_codex":[0.9998682,0.00001310224,0.000008053192,0.00002625136,0.00006421706,0.00002014724],"domain_scores_gemma":[0.9998416,0.00003427698,0.00005119539,0.0000136018,0.00004262175,0.00001657702],"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":[0.00001239892,0.000003819143,0.00003431546,0.00001221887,0.000001426875,0.00001044428,0.0000126824,0.00001842785,0.9994264,0.00001757655,0.00000784566,0.0004425461],"study_design_scores_gemma":[0.000003787382,0.00009979978,0.00165301,0.000002612816,0.00000657525,0.00005247882,0.00001555815,0.0008514644,0.9970154,0.00001085552,0.0002833476,0.000005165266],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9877138,0.0006746985,0.01025227,0.000057362,0.00002175226,0.00002656626,0.00007158813,0.0001086499,0.001073238],"genre_scores_gemma":[0.9894285,0.0003169409,0.008976134,0.00002903935,0.00000740614,0.00002245685,0.00006992337,0.00002047053,0.001129149],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001110066,"threshold_uncertainty_score":0.002954066,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00855077298570829,"score_gpt":0.2113716818846615,"score_spread":0.2028209088989532,"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."}}