{"id":"W2530066498","doi":"10.1016/j.cofs.2016.09.009","title":"Molecular gels: improving selection and design through computational methods","year":2016,"lang":"en","type":"article","venue":"Current Opinion in Food Science","topic":"Supramolecular Self-Assembly in Materials","field":"Materials Science","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Biochemical engineering; Rational design; Selection (genetic algorithm); Computer science; Nanotechnology; Supramolecular chemistry; Identification (biology); Materials science; Chemistry; Artificial intelligence; Biology; Engineering; Molecule; Organic chemistry","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":[0.0009771936,0.0007492912,0.0009271437,0.0007280122,0.0003805025,0.0008896005,0.001142021,0.000914446,0.004397803],"category_scores_gemma":[0.002251579,0.0004441305,0.0005871967,0.0004821097,0.0003587698,0.0009698462,0.0004468961,0.001045581,0.0008206411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004604226,"about_ca_system_score_gemma":0.000920839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00125817,"about_ca_topic_score_gemma":0.002315394,"domain_scores_codex":[0.9998199,0.00008361227,0.000008433312,0.00002514588,0.00004451362,0.00001846197],"domain_scores_gemma":[0.9991372,0.0006227437,0.00006295933,0.00005241026,0.00009585478,0.0000287441],"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":[0.0002483371,0.0002787566,0.001132156,0.0005663325,0.00009320702,0.000145225,0.00005895762,0.8738955,0.006834278,0.03164833,0.00376469,0.0813342],"study_design_scores_gemma":[0.00006098397,0.0000381399,0.00003850718,0.00001694417,0.00002084285,0.00001101046,0.000009376913,0.9929045,0.001581048,0.004054961,0.001257318,0.000006406158],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.2819875,0.003546763,0.6771638,0.002059319,0.0002830819,0.0003212874,0.001121288,0.003337259,0.03017977],"genre_scores_gemma":[0.6158465,0.001684586,0.3769186,0.0004938943,0.00007951316,0.0006196413,0.0007348785,0.0004680104,0.003154338],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.004397803,"threshold_uncertainty_score":0.01471215,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09621575714725028,"score_gpt":0.3996386411803471,"score_spread":0.3034228840330969,"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."}}