{"id":"W1872559647","doi":"10.1139/x11-081","title":"Quantitative magnetic resonance measurements of low moisture content wood<sup>1</sup>This article is a contribution to the series The Role of Sensors in the New Forest Products Industry and Bioeconomy.","year":2011,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"NMR spectroscopy and applications","field":"Physics and Astronomy","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Wood Council; University of New Brunswick","funders":"","keywords":"Water content; Environmental science; Wood industry; Moisture; Magnetic resonance imaging; Spectroscopy; Process engineering; Materials science; Engineering; Physics; Forestry; Composite material; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.001108372,0.00008127427,0.0001446903,0.0001071588,0.0001992516,0.00004692603,0.0003783412,0.00004104298,0.00008876825],"category_scores_gemma":[0.000127336,0.00004597457,0.00003928733,0.0004196457,0.000333307,0.000111902,0.0000216963,0.0005216684,0.000004544322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004804183,"about_ca_system_score_gemma":0.0007548849,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02379153,"about_ca_topic_score_gemma":0.03408817,"domain_scores_codex":[0.9988798,0.0001816529,0.0002895463,0.0001115937,0.000238791,0.0002986244],"domain_scores_gemma":[0.9988458,0.00009491038,0.0001228609,0.0002398191,0.0005241676,0.0001725101],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001695321,0.00007415509,0.9051178,0.00001087179,0.00004878367,0.000003602676,0.01712995,0.0001155665,0.001304373,0.07129655,0.002606886,0.002121975],"study_design_scores_gemma":[0.0008676265,0.001044229,0.8808146,0.0002365536,0.00003400848,0.00001758247,0.02869466,0.0001373562,0.05516365,0.02066126,0.01218945,0.0001390898],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9896827,0.001328395,0.00001855032,0.007738992,0.00001233127,0.0005213226,0.00005073063,5.353135e-7,0.0006464101],"genre_scores_gemma":[0.9995822,0.000007592935,0.0001224059,0.00004443294,0.00007825801,0.00002447715,0.000001222068,0.000005913257,0.0001334697],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05385927,"threshold_uncertainty_score":0.9835372,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06313037051964758,"score_gpt":0.3212814540067284,"score_spread":0.2581510834870808,"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."}}