{"id":"W2630095112","doi":"","title":"Improving sawmill agility through log classification","year":2012,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Forest Biomass Utilization and Management","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Coproduction; Production (economics); Raw material; Mill; Context (archaeology); Yard; Process (computing); Computer science; Engineering; Agricultural engineering; Geography; Mechanical engineering; Economics","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":[0.0005050027,0.0009481713,0.0008605804,0.001443613,0.0004034421,0.001216373,0.0009829646,0.0006722628,0.006473283],"category_scores_gemma":[0.002612885,0.000287662,0.0003888742,0.001200891,0.000240197,0.001419765,0.000667103,0.0007779807,0.002890826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003049419,"about_ca_system_score_gemma":0.0005191638,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002290199,"about_ca_topic_score_gemma":0.003628158,"domain_scores_codex":[0.9996638,0.00003805181,0.00001794689,0.0001012717,0.0001241399,0.00005470271],"domain_scores_gemma":[0.9980279,0.0007888817,0.0002205387,0.0003293443,0.0005131528,0.0001201557],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001313686,0.0007456958,0.02129492,0.0001187782,0.00004340157,0.0001098725,0.00006390531,0.0919173,0.06778207,0.0003963627,0.005092661,0.8111214],"study_design_scores_gemma":[0.00003181348,0.0002043879,0.009857779,0.00001109021,0.00003090905,0.00004546905,0.00007977083,0.9616183,0.02486487,0.001563986,0.001672229,0.00001935417],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5937143,0.0004768342,0.3809474,0.0005134768,0.0003225306,0.0001200954,0.001580244,0.01499579,0.007329295],"genre_scores_gemma":[0.9377516,0.0001214986,0.05502531,0.00008176967,0.00009260028,0.00003711736,0.001435464,0.0003358638,0.00511877],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006473283,"threshold_uncertainty_score":0.02165526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02227025060324718,"score_gpt":0.2254383333087551,"score_spread":0.2031680827055079,"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."}}