{"id":"W1976682825","doi":"10.1007/s00226-010-0316-8","title":"Dielectric mixing models for water content determination in woody biomass","year":2010,"lang":"en","type":"article","venue":"Wood Science and Technology","topic":"Wood Treatment and Properties","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Santen","keywords":"Dielectric; Mixing (physics); Biomass (ecology); Water content; Materials science; Content (measure theory); Range (aeronautics); Soil science; Environmental science; Composite material; Mathematics; Physics; Geotechnical engineering; Geology; Mathematical analysis","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":[0.0006229167,0.0006107009,0.0004656396,0.0004446617,0.0003271701,0.0005558422,0.0008459722,0.0007961653,0.000927506],"category_scores_gemma":[0.001906097,0.0003475644,0.0007438574,0.0004798168,0.0002231349,0.001172419,0.0004603339,0.0008103494,0.00047098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004183641,"about_ca_system_score_gemma":0.000253913,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001607636,"about_ca_topic_score_gemma":0.001395247,"domain_scores_codex":[0.9998496,0.00004894753,0.000009037379,0.00002480095,0.00004979621,0.00001773102],"domain_scores_gemma":[0.9994492,0.0003723109,0.00005110941,0.00004127802,0.00006350449,0.00002258573],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001267885,0.0001013572,0.0006924858,0.00008840518,0.000048346,0.00006118178,0.00006008849,0.922694,0.032408,0.01469835,0.0004640711,0.02855694],"study_design_scores_gemma":[0.000002740119,0.000008395597,0.00006430977,0.000001493501,0.000004406572,0.000007213911,0.000002644007,0.9964902,0.002023681,0.001180501,0.0002100395,0.000004297131],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04742505,0.0003906222,0.949443,0.00008305595,0.00002680357,0.00004382705,0.00009918554,0.0003561164,0.002132356],"genre_scores_gemma":[0.8651007,0.001216391,0.1194533,0.0001197105,0.00007026768,0.0002982492,0.0004428473,0.0002714896,0.01302715],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001607636,"threshold_uncertainty_score":0.003294349,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02014888683999595,"score_gpt":0.2135847004946642,"score_spread":0.1934358136546682,"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."}}