{"id":"W4392193517","doi":"10.56042/ijems.v30i6.502","title":"Regression and Cluster Analysis of GGBS based geopolymer composite at different proportion of Ceramic Dust","year":2023,"lang":"en","type":"article","venue":"Indian Journal of Engineering and Materials Sciences","topic":"Advanced ceramic materials synthesis","field":"Materials Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Geopolymer; Composite number; Cluster (spacecraft); Regression analysis; Ceramic; Ground granulated blast-furnace slag; Mathematics; Composite material; Materials science; Statistics; Fly ash; Computer science","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":[],"consensus_categories":[],"category_scores_codex":[0.00101926,0.0001570471,0.0006052153,0.0007131404,0.00009442114,0.00006224429,0.0001807017,0.00006112458,0.0001581366],"category_scores_gemma":[0.0001029378,0.0001071515,0.00006111739,0.0004313441,0.0002635379,0.0002095793,0.00008946843,0.00003650606,0.000001234969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000025396,"about_ca_system_score_gemma":0.00002399303,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002372124,"about_ca_topic_score_gemma":0.00000303019,"domain_scores_codex":[0.9984022,0.0000887988,0.0007048766,0.0001942525,0.0003806069,0.0002292379],"domain_scores_gemma":[0.9988462,0.0001632232,0.0006968097,0.0001212797,0.0000796591,0.00009285318],"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":[0.00006496203,0.00001523105,0.001358206,0.0001795228,0.00003551256,0.000009456825,0.0002466127,0.007696155,0.9902168,0.00003658421,0.000006417451,0.0001345235],"study_design_scores_gemma":[0.0002364928,0.0001345203,0.02457264,0.0002964872,0.0001491757,0.00003062674,0.00006338819,0.002291176,0.9720559,0.00004666201,0.000008014103,0.0001149898],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986802,0.0001301527,0.0004271219,0.00009547792,0.0004963782,0.00008164109,0.00006552639,0.00001883521,0.000004650595],"genre_scores_gemma":[0.9986405,0.00007861706,0.001200218,0.000008572291,0.00004188017,0.000002494181,0.000004041336,0.000009817503,0.00001379951],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02321444,"threshold_uncertainty_score":0.4369511,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01102865309297818,"score_gpt":0.2430886116440251,"score_spread":0.2320599585510469,"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."}}