{"id":"W2114085819","doi":"10.1177/1097196304042117","title":"A Comparison of Different Techniques to Quantify Moisture Content Profiles in Porous Building Materials","year":2004,"lang":"en","type":"article","venue":"Journal of Thermal Envelope and Building Science","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":124,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Water content; Measure (data warehouse); Thermal diffusivity; Materials science; Reliability (semiconductor); Moisture; Porosity; Attenuation; Work (physics); Porous medium; Process engineering; Environmental science; Building material; Composite material; Computer science; Geotechnical engineering; Mechanical engineering; Engineering; Optics; Thermodynamics; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008792661,0.0001410067,0.0003731835,0.0001839908,0.0001165837,0.00006798336,0.0003727066,0.00005541011,0.000007406182],"category_scores_gemma":[0.0001217508,0.00009194374,0.00003915248,0.0003834403,0.0003477499,0.0002918985,0.0002183198,0.0001644529,0.000001122452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001856868,"about_ca_system_score_gemma":0.00004159326,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004946993,"about_ca_topic_score_gemma":0.00007801685,"domain_scores_codex":[0.998435,0.00003435888,0.000540236,0.0002103036,0.0005079083,0.0002722075],"domain_scores_gemma":[0.9993376,0.00003197625,0.0003539877,0.0001168359,0.00004038816,0.0001192842],"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":[0.00004130471,0.00007203397,0.07065053,0.00001000556,0.000003807695,0.00001511033,0.0009839825,0.0006650187,0.9109334,0.0001470904,0.000006754283,0.016471],"study_design_scores_gemma":[0.0001417358,0.0001318029,0.4008757,0.000228168,0.000005850712,0.00004967545,0.0001899345,0.000008121287,0.598087,0.0001699258,0.00003269916,0.0000794455],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9981488,0.00008696323,0.0005201311,0.0005857351,0.0001899247,0.0001530151,4.94304e-7,0.000008478974,0.0003064535],"genre_scores_gemma":[0.9732111,0.00001984521,0.02656932,0.0001379863,0.00004785413,4.96258e-7,7.045118e-8,0.000007484806,0.000005899544],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3302252,"threshold_uncertainty_score":0.3749357,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03091774726412537,"score_gpt":0.3070754774532451,"score_spread":0.2761577301891197,"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."}}