{"id":"W4313828952","doi":"10.3390/buildings13010172","title":"Physico-Antibacterial Feature and SEM Morphology of Bio-Hydraulic Lime Mortars Incorporating Nano-Graphene Oxide and Binary Combination of Nano-Graphene Oxide with Nano Silver, Fly Ash, Zinc, and Titanium Powders","year":2023,"lang":"en","type":"article","venue":"Buildings","topic":"Building materials and conservation","field":"Earth and Planetary Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Colegiul Consultativ pentru Cercetare-Dezvoltare şi Inovare; Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii; Ministerul Cercetării, Inovării şi Digitalizării","keywords":"Mortar; Materials science; Fly ash; Lime; Lime mortar; Oxide; Composite material; Titanium oxide; Nano-; Chemical engineering; Metallurgy","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":[0.0001026072,0.0002631271,0.0001133993,0.0005165194,0.0001386874,0.0001918205,0.0001467976,0.0002594943,0.0009077041],"category_scores_gemma":[0.0001354444,0.000161075,0.0002383309,0.0002109275,0.0002270801,0.0001508236,0.00008998996,0.0002162358,0.0002237376],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001208545,"about_ca_system_score_gemma":0.00006656317,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008829046,"about_ca_topic_score_gemma":0.00218395,"domain_scores_codex":[0.9999142,0.000006240862,0.000007968068,0.00001927464,0.0000349525,0.00001734239],"domain_scores_gemma":[0.9998957,0.0000188847,0.00003367043,0.00000676952,0.00003027119,0.00001480939],"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":[0.00004740014,0.00001419892,0.000444271,0.000029774,0.000004180501,0.00005753315,0.0000194817,0.00009123956,0.9985849,0.00002764064,0.00001511201,0.0006643317],"study_design_scores_gemma":[0.000004634088,0.0002759232,0.03517486,0.00001065353,0.00002931707,0.000198597,0.0001024423,0.0008318837,0.9625532,0.00002649895,0.0007816085,0.00001046675],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973393,0.0003951409,0.0008010085,0.00001632468,0.00001269291,0.00001223989,0.0002195587,0.00003159365,0.001172031],"genre_scores_gemma":[0.9970568,0.0002476622,0.00104417,0.00002037703,0.000006374335,0.00001376458,0.000225349,0.00001101073,0.001374387],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009077041,"threshold_uncertainty_score":0.003036499,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00886294717008965,"score_gpt":0.1970575904893642,"score_spread":0.1881946433192745,"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."}}