{"id":"W1997259427","doi":"10.1063/1.1289217","title":"Spatially resolved pore-size distribution of drying concrete with magnetic resonance imaging","year":2000,"lang":"en","type":"article","venue":"Journal of Applied Physics","topic":"Concrete and Cement Materials Research","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Water content; Materials science; Moisture; Freezing-point depression; Pore water pressure; Freezing point; Cylinder; Mineralogy; Composite material; Nuclear magnetic resonance; Chemistry; Geology; Thermodynamics; Geotechnical engineering; Geometry","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":[0.0001886639,0.0001459017,0.000169123,0.0003592669,0.000125661,0.0001554257,0.0001712649,0.0001800412,0.0003521892],"category_scores_gemma":[0.0003703566,0.0001558165,0.0000983856,0.000200858,0.0002805823,0.0002780262,0.0001471178,0.0002692236,0.00007504276],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001610628,"about_ca_system_score_gemma":0.000101167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001112133,"about_ca_topic_score_gemma":0.001467894,"domain_scores_codex":[0.9999201,0.000005808854,0.000002932061,0.00001996425,0.00003632296,0.00001477932],"domain_scores_gemma":[0.9997467,0.00007476239,0.00007379385,0.00002097786,0.00006138805,0.00002230927],"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":[0.00001865459,0.000003203472,0.0003750366,0.00001606338,0.000002696185,0.0000329451,0.0000225833,0.0002909244,0.9982548,0.00003581629,0.000008312424,0.0009388141],"study_design_scores_gemma":[0.000006229503,0.000118122,0.02711046,0.000007536816,0.00001723864,0.0004650878,0.00004629725,0.008658123,0.9629661,0.00007075186,0.0005168241,0.00001719292],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9901127,0.0006107825,0.0086502,0.00001381372,0.000003803224,0.000007221879,0.00007783082,0.00006081782,0.000462897],"genre_scores_gemma":[0.9945965,0.0002408706,0.004721038,0.00000717471,0.000003882562,0.000004845877,0.00006733589,0.00001236994,0.0003460248],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001112133,"threshold_uncertainty_score":0.002211273,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006337084976413608,"score_gpt":0.2071003756608753,"score_spread":0.2007632906844617,"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."}}