{"id":"W4237770426","doi":"10.1515/energyo.0149.00009","title":"Effect of LC refining intensity on fractionated and unfractionated mechanical pulp","year":2019,"lang":"en","type":"dataset","venue":"","topic":"Lignin and Wood Chemistry","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Pulp (tooth); Refining (metallurgy); Intensity (physics); Pulp and paper industry; Chemistry; Chromatography; Biomedical engineering; Medicine; Dentistry; Engineering; Physics","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":[0.001188178,0.002632804,0.001384143,0.003776293,0.0007847429,0.001697931,0.001958485,0.002486977,0.02255062],"category_scores_gemma":[0.004854316,0.0004784979,0.002365001,0.004059833,0.0004668986,0.0009679326,0.001262509,0.001213241,0.02959896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001113031,"about_ca_system_score_gemma":0.001655815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03689072,"about_ca_topic_score_gemma":0.07007551,"domain_scores_codex":[0.9988612,0.0001678378,0.0001089193,0.000458017,0.0002571574,0.0001467956],"domain_scores_gemma":[0.998143,0.0007632461,0.0001663312,0.0003886118,0.0004365629,0.000102295],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007741699,0.0001929919,0.00682305,0.003967278,0.0004272615,0.0001213484,0.000066458,0.003824914,0.001428802,0.0005907333,0.9600009,0.02178207],"study_design_scores_gemma":[0.001074908,0.0001452725,0.03835038,0.001110606,0.0005997907,0.0002068096,0.0002081337,0.004490118,0.003490102,0.002336396,0.9478609,0.0001265763],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00187606,0.000516422,0.0001515042,0.00007668326,0.00003725152,0.00001363793,0.9960455,0.0004760818,0.0008068956],"genre_scores_gemma":[0.00169741,0.0001595702,0.0005570549,0.00005126579,0.000006089796,0.00003740249,0.9966505,0.0000524369,0.0007881473],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03689072,"threshold_uncertainty_score":0.07543933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007566117305403993,"score_gpt":0.237846389257124,"score_spread":0.23028027195172,"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."}}