{"id":"W3096943259","doi":"10.24215/16666038.20.e08","title":"Data Science &amp; Engineering into Food Science: A novel Big Data Platform for Low Molecular Weight Gelators’ Behavioral Analysis","year":2020,"lang":"en","type":"article","venue":"Journal of Computer Science and Technology","topic":"Supramolecular Self-Assembly in Materials","field":"Materials Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Big data; Data science; Scalability; Science and engineering; Limiting; Homogenization (climate); Data mining; Database","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":"codex-gemma-dda1882f352a","candidate_categories":["sts","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.005259906,0.0002596198,0.000597638,0.002435967,0.000596714,0.001046792,0.01062263,0.0001237317,0.000006156606],"category_scores_gemma":[0.001158079,0.0002198314,0.00005492556,0.009059013,0.002911814,0.003849098,0.005932511,0.0002486628,0.000005467907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001506161,"about_ca_system_score_gemma":0.001897853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000717101,"about_ca_topic_score_gemma":0.000007608587,"domain_scores_codex":[0.9954682,0.00001216459,0.0007745723,0.001259571,0.001683763,0.0008017361],"domain_scores_gemma":[0.9956871,0.0000636985,0.0005530231,0.001973547,0.001308012,0.0004146679],"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":[0.00001449996,0.00006832451,0.0002227792,0.00002097709,0.00002971346,0.00001604103,0.0001555354,0.0002336209,0.9937849,0.00152483,0.00003305468,0.003895717],"study_design_scores_gemma":[0.0007904368,0.0009497914,0.0002782996,0.0000535187,0.0003136411,0.0003073752,0.00007360306,0.108797,0.8854319,0.0005315163,0.002033456,0.0004395169],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6332361,0.00009382869,0.3646883,0.0008217255,0.0009060405,0.0001463438,0.00004949933,0.0000564183,0.000001760362],"genre_scores_gemma":[0.7616785,0.000009956389,0.2379494,0.0001575503,0.000184147,0.000003111206,0.000005206538,0.0000118832,1.878225e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1284425,"threshold_uncertainty_score":0.9999902,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06046403470218005,"score_gpt":0.3055245867752199,"score_spread":0.2450605520730399,"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."}}