{"id":"W4403380166","doi":"10.3390/f15101800","title":"Preliminary Insights on Moisture Content Measurement in Square Timbers Using GPR Signals and 1D-CNN Models","year":2024,"lang":"en","type":"article","venue":"Forests","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Fundamental Research Funds for the Central Universities; China Scholarship Council","keywords":"Mean squared error; Ground-penetrating radar; Convolutional neural network; Water content; Waveform; Computer science; Reliability (semiconductor); Artificial neural network; Environmental science; Remote sensing; Pattern recognition (psychology); Soil science; Radar; Artificial intelligence; Mathematics; Statistics; Engineering; Geology; Geotechnical engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003016134,0.0005585023,0.000174245,0.0004406348,0.00008483782,0.0003083586,0.0003178322,0.000377227,0.0007425889],"category_scores_gemma":[0.0005981292,0.0001547038,0.0002929742,0.0003912327,0.0001693894,0.0005879595,0.0001922279,0.0002666367,0.0002386862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002609643,"about_ca_system_score_gemma":0.000218915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007779153,"about_ca_topic_score_gemma":0.01222712,"domain_scores_codex":[0.9999214,0.000008693628,0.000003797041,0.00002765028,0.00001972356,0.00001876152],"domain_scores_gemma":[0.9998924,0.00004113525,0.00001174416,0.00001014724,0.00003712045,0.000007504242],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004029857,0.0002149638,0.05186541,0.0002481745,0.0001047542,0.0008004589,0.000185618,0.5689537,0.09955771,0.002583101,0.002479368,0.2726038],"study_design_scores_gemma":[0.00000221776,0.00002244332,0.006810131,0.000006011526,0.00000973696,0.00004329348,0.00002979487,0.9866862,0.005735098,0.0003207175,0.0003266021,0.000007678593],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8314437,0.001094038,0.1610689,0.0002990935,0.00008282313,0.00004605693,0.0007734795,0.00072981,0.004462054],"genre_scores_gemma":[0.9737906,0.0003881587,0.02416947,0.00005148957,0.00001440826,0.00001384805,0.0004526989,0.00002345362,0.001095866],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007779153,"threshold_uncertainty_score":0.01546776,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09522477789281861,"score_gpt":0.2785725476740988,"score_spread":0.1833477697812802,"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."}}