{"id":"W2594142768","doi":"10.1080/14942119.2017.1297521","title":"A NIR machine for moisture content measurements of forest biomass in frozen and unfrozen conditions","year":2017,"lang":"en","type":"article","venue":"International Journal of Forest Engineering","topic":"Wood Treatment and Properties","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"FPInnovations","funders":"","keywords":"Biomass (ecology); Water content; Heat of combustion; Environmental science; Combustion; Repeatability; Moisture; Pulp and paper industry; Materials science; Composite material; Mathematics; Agronomy; Chemistry; Geotechnical engineering; Statistics; Geology; Engineering","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":[0.001238717,0.0007821088,0.0006595266,0.001410965,0.00053945,0.0003867069,0.0007965059,0.0006924664,0.006069606],"category_scores_gemma":[0.001481578,0.0004467278,0.000423111,0.001382533,0.000363895,0.0008942995,0.000467617,0.000693356,0.002140065],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002721749,"about_ca_system_score_gemma":0.0004123484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000378624,"about_ca_topic_score_gemma":0.001374083,"domain_scores_codex":[0.9986998,0.0001576202,0.00006754936,0.0003920054,0.0006351782,0.00004786049],"domain_scores_gemma":[0.9988054,0.0004749956,0.0001493952,0.0002150794,0.0002964314,0.0000586499],"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":[0.0006082673,0.000259667,0.01458489,0.0003911443,0.00005418104,0.0001222537,0.0002425332,0.001009428,0.8279465,0.0006724891,0.002696254,0.1514123],"study_design_scores_gemma":[0.0001488652,0.00218223,0.2909654,0.0001372503,0.0003246641,0.003213129,0.000327168,0.04910354,0.6085342,0.001268252,0.04357742,0.0002179163],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4831033,0.001135577,0.4932572,0.0001219292,0.0004185063,0.00084941,0.005043682,0.005452548,0.01061777],"genre_scores_gemma":[0.4801918,0.0004496571,0.5040719,0.000200401,0.00009325746,0.001034691,0.002372179,0.0003892649,0.01119677],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006069606,"threshold_uncertainty_score":0.02030486,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04294717903318524,"score_gpt":0.2553313829909287,"score_spread":0.2123842039577434,"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."}}