{"id":"W4297092833","doi":"10.1073/pnas.2209213119","title":"Nuclear magnetic resonance diffraction with subangstrom precision","year":2022,"lang":"lv","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Atomic and Subatomic Physics Research","field":"Physics and Astronomy","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; University of Waterloo","funders":"Army Research Office; Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund; University of Waterloo; Industry Canada","keywords":"Algorithm; Materials science; Computer 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":[0.00308283,0.0008801038,0.001070423,0.0009759333,0.0009650191,0.001808507,0.001575216,0.001256987,0.00731676],"category_scores_gemma":[0.003726167,0.001056365,0.0005029857,0.001401114,0.000989725,0.001341484,0.001449207,0.003004448,0.005778488],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001839863,"about_ca_system_score_gemma":0.00189343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004609914,"about_ca_topic_score_gemma":0.01258837,"domain_scores_codex":[0.997408,0.0004137927,0.0001522345,0.0005241376,0.00131399,0.0001879763],"domain_scores_gemma":[0.9967207,0.0006081898,0.0002411186,0.001272399,0.0009816473,0.000175878],"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":[0.0002876171,0.0002096627,0.001429689,0.0002700928,0.00007553576,0.000151946,0.0004822489,0.002624237,0.8704103,0.02097507,0.02503144,0.07805213],"study_design_scores_gemma":[0.000176427,0.0001823623,0.001490022,0.0000464359,0.00004158238,0.0002446334,0.000153882,0.02755597,0.8369072,0.003905107,0.1291692,0.000127173],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1425888,0.003436712,0.773003,0.006831688,0.002829359,0.000552065,0.006520303,0.01330752,0.05093055],"genre_scores_gemma":[0.2151177,0.00212566,0.746677,0.0009539897,0.0002614417,0.0004268331,0.003982554,0.001309685,0.02914519],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00731676,"threshold_uncertainty_score":0.02447701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02866309794895344,"score_gpt":0.2908276453029847,"score_spread":0.2621645473540312,"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."}}