{"id":"W3096943259","doi":"10.24215/16666038.20.e08","title":"Data Science & 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007501399,0.002662405,0.002301913,0.005555873,0.001646695,0.008586115,0.005224218,0.002424794,0.005493267],"category_scores_gemma":[0.01781787,0.001386755,0.001734765,0.005793242,0.001813159,0.01069998,0.01025146,0.00411524,0.00496603],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001275949,"about_ca_system_score_gemma":0.003015128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001601903,"about_ca_topic_score_gemma":0.001729329,"domain_scores_codex":[0.993769,0.001106822,0.0008154217,0.001507559,0.002505518,0.0002956407],"domain_scores_gemma":[0.9830574,0.004001883,0.001339939,0.007416688,0.002826233,0.001357809],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002312263,0.001071438,0.0165188,0.002799675,0.0007294419,0.001707525,0.002503877,0.01556513,0.0572646,0.103373,0.09791587,0.6982384],"study_design_scores_gemma":[0.0002438529,0.0004670549,0.007135021,0.000566863,0.0002112132,0.0008642597,0.001173213,0.343603,0.0885504,0.2618201,0.2948974,0.0004675362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006100562,0.001542393,0.9159689,0.003625246,0.0005915318,0.0008060456,0.0121263,0.05389289,0.005346101],"genre_scores_gemma":[0.08418623,0.002108526,0.8703907,0.002293194,0.000527952,0.002056665,0.02973745,0.003402427,0.005296839],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008586115,"threshold_uncertainty_score":0.03967166,"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."}}