{"id":"W1994821011","doi":"10.15376/biores.8.3.3385-3398","title":"Improved Fiber Separation and Energy Reduction in Thermomechanical Pulp Refining Using Enzyme-Pretreated Wood","year":2013,"lang":"en","type":"article","venue":"BioResources","topic":"Advanced Cellulose Research Studies","field":"Materials Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"National Science Council","keywords":"Middle lamella; Refining (metallurgy); Materials science; Pulp (tooth); Composite material; Pulp and paper industry; Lignin; Penetration (warfare); Softwood; Chemistry; Metallurgy; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002112694,0.0001317317,0.000168836,0.0001076675,0.0001454961,0.00009613144,0.00009183973,0.00007940367,0.0002225572],"category_scores_gemma":[0.00008893585,0.0001055415,0.00002042617,0.0001913821,0.0001184869,0.0002869711,0.0001235635,0.00007627266,0.00002362165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007353802,"about_ca_system_score_gemma":0.00001598567,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001524978,"about_ca_topic_score_gemma":0.00004952491,"domain_scores_codex":[0.9988497,0.0001246737,0.0002118109,0.0003283013,0.000169855,0.0003155966],"domain_scores_gemma":[0.9995759,0.00006405359,0.00008483434,0.0001516301,0.0000551295,0.00006842164],"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":[0.00002401244,0.00001782603,0.00007455007,0.00000867492,0.000004782254,0.00000165912,0.0003401857,0.0000668535,0.9909917,0.00004343752,0.00002184222,0.008404518],"study_design_scores_gemma":[0.0005116977,0.0001541883,0.001199341,0.00006614,0.00001079789,0.00001701741,0.0007967314,0.01638626,0.9791276,0.000435068,0.001026974,0.0002681634],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9983778,0.0006921264,0.0002748666,0.00008071116,0.00005200292,0.00008099296,0.000002813888,0.00005828957,0.0003803828],"genre_scores_gemma":[0.9971444,0.00005164593,0.001883399,0.00001143093,0.00008851369,0.00004784392,0.000003634569,0.00001503601,0.0007541021],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0163194,"threshold_uncertainty_score":0.4303858,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02559847598427661,"score_gpt":0.2873306276029561,"score_spread":0.2617321516186795,"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."}}