{"id":"W2333411952","doi":"10.1021/ef300884k","title":"Chemical Composition of Wood Chips and Wood Pellets","year":2012,"lang":"en","type":"article","venue":"Energy & Fuels","topic":"Thermochemical Biomass Conversion Processes","field":"Engineering","cited_by":117,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pellets; Pellet; Inductively coupled plasma mass spectrometry; Inductively coupled plasma; Contamination; Pulp and paper industry; Heat of combustion; Mercury (programming language); Water content; Chemical composition; Environmental chemistry; Environmental science; Chemistry; Combustion; Materials science; Mass spectrometry; Composite material","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0001635369,0.0004728599,0.0002393201,0.002741192,0.0004775875,0.0005664895,0.0002454203,0.0001933217,0.00153413],"category_scores_gemma":[0.0002470116,0.0002290553,0.0001949438,0.002314132,0.0002480326,0.000193777,0.0001653512,0.0001235903,0.0003102944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003682049,"about_ca_system_score_gemma":0.0003500863,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01472979,"about_ca_topic_score_gemma":0.03271261,"domain_scores_codex":[0.999665,0.00001976372,0.00002775678,0.00008433329,0.0001521746,0.00005098747],"domain_scores_gemma":[0.9997627,0.00002422517,0.00003734543,0.00001181625,0.0001378277,0.00002614147],"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":[0.0005123327,0.00008453622,0.154041,0.0003102095,0.0001428785,0.0006582359,0.000284292,0.0007738279,0.8111308,0.0001783893,0.000221545,0.03166194],"study_design_scores_gemma":[0.0000106444,0.0002497522,0.8435174,0.00003333133,0.0001004277,0.0005598363,0.0005147236,0.0007144947,0.1496859,0.00006503616,0.004527102,0.00002147211],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9944787,0.0008783984,0.0008143738,0.000005392671,0.000006027363,0.00004284743,0.001443632,0.00001744902,0.002313045],"genre_scores_gemma":[0.9890564,0.001237111,0.003723349,0.00004035579,0.000008175441,0.00007561735,0.003088676,0.00004053601,0.002729753],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01472979,"threshold_uncertainty_score":0.02928811,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007155243780872717,"score_gpt":0.1899213380039098,"score_spread":0.1827660942230371,"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."}}