{"id":"W4397005312","doi":"10.1515/npprj-2024-0004","title":"Estimating lags in a kraft mill","year":2024,"lang":"en","type":"article","venue":"Nordic Pulp & Paper Research Journal","topic":"Mineral Processing and Grinding","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mill; Kraft paper; Kraft process; Pulp and paper industry; Industrial chemistry; Environmental science; Process engineering; Waste management; Engineering; Biochemical engineering; Mechanical engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.001492791,0.0007322347,0.0004492537,0.0004806192,0.0004122592,0.001188722,0.0005813642,0.0005736093,0.00173883],"category_scores_gemma":[0.00474341,0.0005626855,0.0006544243,0.0007197223,0.0004316311,0.00088947,0.0004922546,0.001068489,0.0003583343],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001438416,"about_ca_system_score_gemma":0.002686585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1302241,"about_ca_topic_score_gemma":0.1158138,"domain_scores_codex":[0.9994537,0.0001119972,0.00002602977,0.0002191562,0.00009620289,0.00009299864],"domain_scores_gemma":[0.9977196,0.001343808,0.0003386122,0.0002299218,0.000267955,0.0001000858],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000347703,0.0001068501,0.1244228,0.0000635063,0.0001505652,0.0001504625,0.0001657098,0.8388839,0.004465947,0.005554569,0.0006873296,0.02500057],"study_design_scores_gemma":[0.00002862076,0.00009187082,0.03750649,0.00001333213,0.00004072745,0.00002327342,0.0001014068,0.9522395,0.003815414,0.004630188,0.001466046,0.00004315967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8348652,0.0001978427,0.1611563,0.000149692,0.00003183584,0.00004148542,0.001270668,0.0005407651,0.001746226],"genre_scores_gemma":[0.982427,0.00007664483,0.01554023,0.00001851034,0.000005248669,0.00001341818,0.0008955233,0.00003165226,0.0009917854],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1302241,"threshold_uncertainty_score":0.2589322,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04624164534708005,"score_gpt":0.365322883614263,"score_spread":0.3190812382671829,"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."}}