{"id":"W4239120073","doi":"10.32920/ryerson.14661045.v1","title":"Investigation of NIR spectroscopy for identifying and sorting wood with respect to species, moisture content, and weathering","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Wood and Agarwood Research","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Weathering; Water content; Spectroscopy; Sorting; Near-infrared spectroscopy; Partial least squares regression; Moisture; Multivariate statistics; Environmental science; Soil science; Materials science; Analytical Chemistry (journal); Chemistry; Environmental chemistry; Geology; Composite material; Mathematics; Optics; Physics; Algorithm","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":[0.001285869,0.0006954064,0.0003289008,0.0007869543,0.0002423407,0.000375296,0.0002702245,0.0003674333,0.0006025074],"category_scores_gemma":[0.0005807271,0.0002114204,0.0003268528,0.000536168,0.0003000969,0.0005949215,0.0002264689,0.000355938,0.0002904891],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001141076,"about_ca_system_score_gemma":0.00021876,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003210201,"about_ca_topic_score_gemma":0.0008867863,"domain_scores_codex":[0.9995177,0.0001477257,0.00001398224,0.0001083476,0.000177519,0.00003480359],"domain_scores_gemma":[0.9996247,0.0001975355,0.00004226021,0.00003651455,0.00007426042,0.00002463945],"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":[0.0001415407,0.0001020291,0.0022784,0.0001243966,0.0000254872,0.00005074272,0.00003781795,0.002779726,0.9541714,0.0003529489,0.00005980596,0.03987575],"study_design_scores_gemma":[0.00001005103,0.000946182,0.0176478,0.00001770985,0.00006198967,0.0003353873,0.00008969196,0.04956481,0.9287959,0.0005399137,0.001957765,0.00003266782],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7928764,0.003654323,0.1989484,0.00009230502,0.00008452189,0.00007312947,0.0001713642,0.0003323027,0.003767302],"genre_scores_gemma":[0.8229868,0.002029583,0.1722589,0.00004549387,0.00004177819,0.00004310684,0.0001459891,0.00005465453,0.00239373],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001285869,"threshold_uncertainty_score":0.006800413,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09768704632008522,"score_gpt":0.309479420492271,"score_spread":0.2117923741721858,"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."}}