{"id":"W2064033125","doi":"10.1007/s00107-014-0856-y","title":"Estimation of moisture content of trembling aspen (Populus tremuloides Michx.) strands by near infrared spectroscopy (NIRS)","year":2014,"lang":"en","type":"article","venue":"European Journal of Wood and Wood Products","topic":"Wood Treatment and Properties","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of New Brunswick; FPInnovations; University of Toronto","funders":"","keywords":"Water content; Near-infrared spectroscopy; Partial least squares regression; Moisture; Gravimetric analysis; Calibration; Spectroscopy; Coefficient of determination; Mean squared error; Analytical Chemistry (journal); Materials science; Environmental science; Botany; Chemistry; Mathematics; Composite material; Environmental chemistry; Geology; Biology; Statistics","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":[0.0000836891,0.0002121445,0.00009708291,0.0004778013,0.0001295842,0.0001801879,0.0001055997,0.0001381604,0.0004162637],"category_scores_gemma":[0.00007991216,0.0001147682,0.0001011125,0.0001838719,0.00007356545,0.000217578,0.00009444686,0.0001850169,0.0001653539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007229541,"about_ca_system_score_gemma":0.00004369794,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001067479,"about_ca_topic_score_gemma":0.002555613,"domain_scores_codex":[0.9999486,0.000003586749,0.000002900251,0.00002149455,0.00001808561,0.000005284926],"domain_scores_gemma":[0.9999264,0.00001291611,0.00002362157,0.000003493895,0.00001995368,0.00001360367],"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":[0.000126906,0.00001950932,0.01125551,0.00001884936,0.00001213824,0.00002796418,0.00004050205,0.00006895618,0.9856366,0.00001164378,0.00001820617,0.002763256],"study_design_scores_gemma":[0.00000781352,0.0003417966,0.4801323,0.000006838863,0.00006076341,0.0001695964,0.0001906642,0.002216209,0.515776,0.00004908912,0.001030539,0.00001844497],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9983366,0.0002249106,0.0007604412,0.000005708263,0.000002351873,0.000003625397,0.0002002224,0.00001282845,0.0004533666],"genre_scores_gemma":[0.9966628,0.0002170889,0.001410705,0.00001438529,0.000002733316,0.000007638359,0.0004360964,0.000007186812,0.001241361],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001067479,"threshold_uncertainty_score":0.002122521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01523245998001452,"score_gpt":0.19528117293713,"score_spread":0.1800487129571155,"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."}}