{"id":"W4413140225","doi":"10.1021/acs.jpcb.5c03717","title":"Data-Driven Design of High-Temperature-Resistant Polyimides Using Hierarchical Gaussian Process Regression","year":2025,"lang":"en","type":"article","venue":"The Journal of Physical Chemistry B","topic":"Synthesis and properties of polymers","field":"Materials Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Key Research and Development Program of Zhejiang Province","keywords":"Kriging; Gaussian process; Computer science; Artificial intelligence; Machine learning; Microelectronics; Regression; Process (computing); Field (mathematics); Gaussian; Materials science; Nanotechnology; Mathematics; Statistics","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":[0.0007440201,0.0005409466,0.0004476976,0.0003351596,0.0001673158,0.0004243028,0.0008505324,0.0003735306,0.0009074749],"category_scores_gemma":[0.001149138,0.0003095357,0.0005250343,0.0002782837,0.0003563015,0.0005246854,0.0004653472,0.0007706125,0.0002510501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000679863,"about_ca_system_score_gemma":0.0009060221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001909474,"about_ca_topic_score_gemma":0.00309679,"domain_scores_codex":[0.9997874,0.00003144098,0.00001043631,0.00005079011,0.00008934087,0.0000305306],"domain_scores_gemma":[0.999705,0.0001114304,0.0000624022,0.00003118401,0.00006864308,0.00002129855],"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":[0.00006092968,0.0001071753,0.0006706383,0.00009784361,0.00002448123,0.00004281292,0.0000275024,0.921647,0.04004115,0.00268069,0.0002965125,0.03430331],"study_design_scores_gemma":[0.000002687496,0.00002940451,0.00006246653,0.000001037702,0.000003095963,0.000001908092,0.000001514048,0.9953608,0.004240797,0.0001692696,0.0001248473,0.000002340877],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1594364,0.0003280623,0.8360474,0.0001228325,0.00003191913,0.0001202743,0.0001108456,0.001234422,0.002567823],"genre_scores_gemma":[0.8572234,0.0002028251,0.1407473,0.00005251947,0.000007596484,0.0001828242,0.0001958053,0.0001085969,0.001279163],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001909474,"threshold_uncertainty_score":0.004932821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0339590319556633,"score_gpt":0.3009081968569398,"score_spread":0.2669491649012765,"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."}}