{"id":"W2994395529","doi":"","title":"Debarking enhancement of frozen logs. Part II: Infrared system for heating logs prior to debarking.","year":2009,"lang":"en","type":"article","venue":"Forest Products Journal","topic":"Wood Treatment and Properties","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Environmental science; Bark (sound); Woodchips; Pulp and paper industry; Forestry; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003805369,0.0002394984,0.0003507374,0.0001558123,0.0003090084,0.00008888532,0.0002140497,0.00006420211,0.00001726422],"category_scores_gemma":[0.000112086,0.000193541,0.00008948578,0.0001901516,0.00001625324,0.0002312315,0.00003460113,0.0001577399,0.0000104807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001819987,"about_ca_system_score_gemma":0.00005748964,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005034077,"about_ca_topic_score_gemma":0.000007627213,"domain_scores_codex":[0.9984765,0.00002552371,0.0005782945,0.0002158319,0.0002465365,0.0004572846],"domain_scores_gemma":[0.9992703,0.0000272767,0.0001337474,0.0002445409,0.0001966169,0.0001275016],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002294688,0.001253288,0.02138055,0.005443878,0.002674541,0.0001934514,0.03276734,0.1134516,0.2803308,0.003603322,0.105589,0.4310175],"study_design_scores_gemma":[0.003423144,0.003972137,0.005396471,0.002938298,0.0003128416,0.0003365126,0.0006829221,0.00493109,0.8770078,0.001359442,0.09850768,0.001131693],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9889772,0.003017846,0.002871974,0.0004888097,0.001368159,0.0008364923,0.000008436788,0.0001590891,0.002271991],"genre_scores_gemma":[0.9869083,0.00004928138,0.01141748,0.00004213844,0.0009148941,0.0000358082,0.00001025773,0.00003657092,0.0005853235],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5966769,"threshold_uncertainty_score":0.7892373,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01792005759912297,"score_gpt":0.2180707462523846,"score_spread":0.2001506886532616,"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."}}