{"id":"W2018898523","doi":"10.1021/ol050830n","title":"Characterization of Porosity in Organic and Metal−Organic Macrocycles by Hyperpolarized <sup>129</sup>Xe NMR Spectroscopy","year":2005,"lang":"en","type":"article","venue":"Organic Letters","topic":"Advanced NMR Techniques and Applications","field":"Chemistry","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Chemistry; Characterization (materials science); Nuclear magnetic resonance spectroscopy; Porosity; Spectroscopy; Metal; Nuclear magnetic resonance; Physical chemistry; Organic chemistry; Nanotechnology; Materials science","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":[0.0000825617,0.00010279,0.00009666379,0.0001977131,0.0001428369,0.0002561141,0.0001266561,0.0001625956,0.0004922729],"category_scores_gemma":[0.0003352485,0.00008034326,0.00006112695,0.0001160598,0.0004386674,0.0002083586,0.000158193,0.0001836879,0.00006208956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001352275,"about_ca_system_score_gemma":0.0001051978,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004128509,"about_ca_topic_score_gemma":0.0006517473,"domain_scores_codex":[0.9999278,0.000006836145,0.000003598851,0.00001536867,0.00002839755,0.00001809736],"domain_scores_gemma":[0.9997913,0.00008488695,0.00004919856,0.00001567372,0.0000280597,0.00003090941],"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":[0.00006230717,0.000005968402,0.0002869045,0.0000178875,0.00000329943,0.00003783131,0.00002839185,0.00018682,0.9979141,0.0001538335,0.00001081496,0.001291822],"study_design_scores_gemma":[0.000008743917,0.0001503696,0.006072321,0.000004759211,0.000007296325,0.0001416092,0.0000489737,0.0007810623,0.9917313,0.00007244846,0.0009736598,0.000007406237],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977889,0.0003532272,0.001076112,0.000009546173,0.000002368509,0.0000072845,0.00008643776,0.00001991169,0.0006560792],"genre_scores_gemma":[0.9982607,0.0002641974,0.000947069,0.000008659371,0.000001713262,0.00001295264,0.0001139973,0.00000765445,0.0003831869],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0004922729,"threshold_uncertainty_score":0.001646817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004681931719792877,"score_gpt":0.2186144186790558,"score_spread":0.213932486959263,"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."}}