{"id":"W4376128502","doi":"10.1080/07373937.2023.2209635","title":"Predicting unit energy consumption during industrial veneer drying via data-driven approaches","year":2023,"lang":"en","type":"article","venue":"Drying Technology","topic":"Wood Treatment and Properties","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Mitacs","keywords":"Veneer; Electricity; Energy consumption; Work (physics); Unit (ring theory); Consumption (sociology); Environmental economics; Computer science; Business; Operations management; Operations research; Environmental science; Economics; Engineering; Mathematics; Mechanical engineering; Sociology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006946496,0.0009354058,0.0006286051,0.0009383247,0.000208574,0.0006641928,0.0008114866,0.000837336,0.001004421],"category_scores_gemma":[0.001845468,0.0003977921,0.0009408977,0.0009432844,0.0001615868,0.0007009138,0.0002243689,0.000716014,0.0004582553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004794275,"about_ca_system_score_gemma":0.000507549,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009910857,"about_ca_topic_score_gemma":0.01541932,"domain_scores_codex":[0.9997889,0.00003792917,0.00001320922,0.00007911145,0.00005556469,0.00002532157],"domain_scores_gemma":[0.9992017,0.0004844421,0.0000633945,0.00005352728,0.0001739814,0.00002291381],"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":[0.000205659,0.0004264509,0.01772521,0.0002181851,0.0000845636,0.0001160349,0.00005163061,0.916196,0.006981696,0.0004516455,0.001055005,0.05648797],"study_design_scores_gemma":[0.000004709236,0.00003345087,0.003575576,0.000006493325,0.000009195535,0.00001278918,0.00001449203,0.9931578,0.00256136,0.0002920787,0.0003213485,0.00001063859],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8408695,0.000506114,0.1507559,0.0002044604,0.00007153577,0.0001272591,0.003662467,0.001861224,0.001941526],"genre_scores_gemma":[0.9582404,0.0001795472,0.03691011,0.00003801614,0.00001729671,0.0001047189,0.003577008,0.0000594021,0.0008734805],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009910857,"threshold_uncertainty_score":0.01970631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1512436266253781,"score_gpt":0.2400130010153521,"score_spread":0.08876937438997404,"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."}}