{"id":"W4362504175","doi":"10.1007/978-3-030-81315-4_23","title":"Sawn Timber Steaming and Drying","year":2023,"lang":"en","type":"book-chapter","venue":"Springer handbooks","topic":"Wood Treatment and Properties","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Kiln; Wood drying; Steaming; Water content; Green wood; Moisture; Pulp and paper industry; Environmental science; Humidity; Dehydration; Materials science; Waste management; Process engineering; Composite material; Engineering; Chemistry; Meteorology; Geography; Geotechnical 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00006115755,0.0003625829,0.0003175866,0.0001455784,0.00009342495,0.00008931795,0.00008634062,0.0002356013,0.0001140698],"category_scores_gemma":[0.000003165265,0.0003383935,0.00008558537,0.00001141556,0.00005188357,0.00005757573,0.00009122221,0.0002915145,0.0006604937],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004933076,"about_ca_system_score_gemma":0.00001096158,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007401567,"about_ca_topic_score_gemma":0.00003734662,"domain_scores_codex":[0.9991308,0.000003181529,0.0002015085,0.0002547087,0.000150581,0.0002592137],"domain_scores_gemma":[0.9995968,0.0000393125,0.00003131512,0.0002376216,0.0000169767,0.00007795549],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002103694,0.00006625475,0.002568036,0.00845343,0.01120752,0.002434658,0.01588678,0.002964887,0.01072131,0.1392821,0.03681361,0.769391],"study_design_scores_gemma":[0.002078034,0.0001835157,0.0003330781,0.00560775,0.0008000087,0.00005236243,0.00007630233,0.002409648,0.01242096,0.01652592,0.9560817,0.003430747],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.00251908,0.007876655,0.00005649816,0.00001630507,0.0007082004,0.0002275051,0.00001538967,0.001058592,0.9875218],"genre_scores_gemma":[0.03097717,0.001334435,0.0002834208,0.00001987619,0.0003716962,0.00002233058,0.00001614563,0.0002826097,0.9666923],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.9192681,"threshold_uncertainty_score":0.9999068,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02298405809870122,"score_gpt":0.179294958192434,"score_spread":0.1563109000937328,"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."}}