{"id":"W4382516672","doi":"10.21203/rs.3.rs-3081771/v1","title":"Xylan derived carbon dots composite with cotton cellulose paper as fluorescence sensor for real time detection Cu2+","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Carbon and Quantum Dots Applications","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Government of Jiangsu Province; National Natural Science Foundation of China","keywords":"Fluorescence; Cellulose; Xylan; Detection limit; Carbon fibers; Materials science; Composite number; Chemistry; Nanotechnology; Chemical engineering; Composite material; Organic chemistry; Chromatography; Optics","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":[0.0001337909,0.0004236714,0.0002214601,0.0002340731,0.0001374743,0.0002601835,0.000272116,0.0004307921,0.00129943],"category_scores_gemma":[0.0001545733,0.0001896677,0.0001706694,0.0002313796,0.0001584929,0.0002452212,0.000191364,0.0002181453,0.0002605074],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003758003,"about_ca_system_score_gemma":0.00020313,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001458762,"about_ca_topic_score_gemma":0.001817891,"domain_scores_codex":[0.9998674,0.00001453616,0.000006420493,0.00004791412,0.00004438949,0.00001930819],"domain_scores_gemma":[0.9999207,0.00001564439,0.0000162044,0.000006761063,0.0000255275,0.00001518685],"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":[0.00002444035,0.000007490241,0.0001203768,0.00002604338,0.000003359098,0.00004618876,0.000005776408,0.0001188254,0.9979441,0.00005367708,0.00005225948,0.001597516],"study_design_scores_gemma":[0.000003652275,0.0000446839,0.0005031584,0.000001433059,0.000004508001,0.00003641523,0.000005973407,0.002435676,0.9964163,0.00001000968,0.0005352595,0.000002897247],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9819365,0.0009429803,0.01458222,0.0001061129,0.0000700548,0.00003317328,0.0001716514,0.0002992217,0.00185812],"genre_scores_gemma":[0.9824907,0.0003868012,0.01402218,0.00004752709,0.00001040263,0.00002521856,0.0001225933,0.00002586503,0.002868717],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001458762,"threshold_uncertainty_score":0.004347086,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04709679018490923,"score_gpt":0.352944711434207,"score_spread":0.3058479212492978,"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."}}