{"id":"W2524328245","doi":"","title":"USING COMPUTED TOMOGRAPHY SCANNING TECHNOLOGY TO EXTRACT VIRTUAL WOOD CORES, DERIVE WOOD DENSITY RADIAL PATTERNS, AND TEST HYPOTHESIS ABOUT DIRECTION, CORE SIZE, AND YEAR OF GROWTH","year":2016,"lang":"en","type":"article","venue":"Wood and Fiber Science (Society of Wood Science and Technology)","topic":"Forest ecology and management","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Natural Resources Canada","funders":"","keywords":"Pith; Bark (sound); Densitometry; Voxel; Calibration; Softwood; Mathematics; Core (optical fiber); Tomography; Materials science; Composite material; Geometry; Botany; Statistics; Computer science; Physics; Artificial intelligence; Biology; Optics; Acoustics","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":[0.0006092303,0.0005918677,0.0002811717,0.001759066,0.0002503948,0.000482655,0.0003104622,0.0002449224,0.0009890689],"category_scores_gemma":[0.00120769,0.0002917726,0.0002827095,0.001289686,0.0004016533,0.0005621473,0.0003554381,0.0002232496,0.0002201166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002020171,"about_ca_system_score_gemma":0.0003186654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002054052,"about_ca_topic_score_gemma":0.00732689,"domain_scores_codex":[0.9996946,0.00006194801,0.00002706979,0.00008510827,0.0001126629,0.00001874237],"domain_scores_gemma":[0.9991876,0.0003324085,0.0001394663,0.0001028787,0.0002051297,0.00003247322],"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":[0.0003181159,0.0001530709,0.1160386,0.0002663982,0.0001241646,0.0002459117,0.0003212513,0.01355633,0.6995705,0.00121901,0.0002860556,0.1679007],"study_design_scores_gemma":[0.00005667755,0.000993429,0.5025757,0.0000529027,0.0002487886,0.002119512,0.0004342075,0.1088721,0.3774671,0.002472438,0.00458418,0.0001230888],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6485621,0.0003908529,0.347808,0.00002775049,0.00002298846,0.0002045045,0.0006146338,0.0003422651,0.002026953],"genre_scores_gemma":[0.7059418,0.0002735683,0.2924815,0.00002481626,0.000008507282,0.0001444299,0.000542688,0.0000376574,0.0005450344],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002054052,"threshold_uncertainty_score":0.00408417,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009138958991367805,"score_gpt":0.2161537107891824,"score_spread":0.2070147517978146,"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."}}