{"id":"W2115099833","doi":"10.1139/cjfr-2014-0545","title":"Measuring foliar moisture content with a moisture analyzer","year":2015,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Forest ecology and management","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundação para a Ciência e a Tecnologia","keywords":"Water content; Moisture; Spectrum analyzer; Environmental science; Green wood; Pulp and paper industry; Wood drying; Chemistry; Materials science; Composite material; Geology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005234523,0.0007128284,0.0006205169,0.001329371,0.0005209791,0.0004181536,0.0007868407,0.0005115957,0.004087493],"category_scores_gemma":[0.0006894486,0.0003483984,0.0002843268,0.001052226,0.0002457511,0.0006694599,0.0005148498,0.00062929,0.001222335],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003274653,"about_ca_system_score_gemma":0.0003339582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001878998,"about_ca_topic_score_gemma":0.004024572,"domain_scores_codex":[0.9991359,0.00007041137,0.00004858312,0.0002927432,0.0004094765,0.00004288664],"domain_scores_gemma":[0.9994993,0.0001318368,0.00008376541,0.00005661465,0.000180926,0.00004757589],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001729598,0.0001128428,0.01269532,0.0001610776,0.00004566364,0.00006029936,0.0001281749,0.0003173537,0.945886,0.0001564052,0.0005517482,0.03971211],"study_design_scores_gemma":[0.0000943149,0.00152056,0.2390495,0.000051985,0.0003027775,0.001209191,0.0002414368,0.02946326,0.7133128,0.0004227937,0.01420019,0.0001311709],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6980658,0.001634381,0.2834329,0.00008334316,0.0001593126,0.0007488141,0.003213898,0.00340001,0.009261544],"genre_scores_gemma":[0.7435985,0.001433825,0.2429984,0.0002679751,0.00009134143,0.0009003872,0.002018516,0.0002706121,0.008420499],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004087493,"threshold_uncertainty_score":0.01367408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09982471496105608,"score_gpt":0.278772790486765,"score_spread":0.1789480755257089,"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."}}