{"id":"W4414081454","doi":"10.1021/acs.analchem.5c03472","title":"A <sup>1</sup>H Background-Free 3D Printing Digital Light Processing Resin for Applications in NMR Spectroscopy","year":2025,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bruker (Canada); University of Toronto","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation; Ministère de l’Environnement, de la Protection de la nature et des Parcs; Krembil Foundation; Washington State University","keywords":"Nuclear magnetic resonance spectroscopy; Proton NMR; Carbon-13 NMR; Solvent; Spectroscopy; NMR spectra database; Fluorine-19 NMR","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003715397,0.0006696758,0.0002750477,0.0004135374,0.0002967796,0.0006672315,0.0005393462,0.0005881641,0.007445292],"category_scores_gemma":[0.0005172816,0.0003966975,0.0004281897,0.0003479324,0.0003373055,0.0005267296,0.0003114316,0.001043049,0.005679021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002685438,"about_ca_system_score_gemma":0.000247692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002164563,"about_ca_topic_score_gemma":0.0005187124,"domain_scores_codex":[0.9996969,0.00002575762,0.00002225041,0.0000721185,0.0001518827,0.00003121377],"domain_scores_gemma":[0.9993451,0.0001773953,0.0001635969,0.0001406781,0.0001286028,0.00004455359],"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":[0.00006486938,0.00001712818,0.0001320877,0.0001608432,0.000006115033,0.0001453142,0.00002890787,0.0001831961,0.9868299,0.0004588145,0.0008565145,0.01111638],"study_design_scores_gemma":[0.000006601692,0.0001299146,0.0008084672,0.00001420851,0.00001586507,0.0004026381,0.00001810649,0.001309945,0.9765882,0.0001007665,0.02058846,0.00001670254],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4989962,0.005979679,0.4243019,0.001019563,0.001543532,0.0004992253,0.005066727,0.01624592,0.04634728],"genre_scores_gemma":[0.6029687,0.004305024,0.3393613,0.001095831,0.000235431,0.000595007,0.003904718,0.003176803,0.04435708],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007445292,"threshold_uncertainty_score":0.02490705,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01443190771591904,"score_gpt":0.336009543866542,"score_spread":0.321577636150623,"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."}}