{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002423821,0.000368764,0.0003048093,0.0001365585,0.0001891323,0.0002659362,0.0008189103,0.0003488612,0.0002682082],"category_scores_gemma":[0.000396745,0.0004141844,0.0001738074,0.0002973543,0.0001523286,0.0002379846,0.0008094711,0.0005022618,0.0001508229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002606116,"about_ca_system_score_gemma":0.00006573916,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003734458,"about_ca_topic_score_gemma":0.000851942,"domain_scores_codex":[0.9969411,0.001171701,0.0005508779,0.0005691536,0.000351055,0.0004161323],"domain_scores_gemma":[0.996567,0.0002476124,0.0002687693,0.001990994,0.0007731045,0.000152584],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002076957,0.001335521,0.008989765,0.00348437,0.000502034,0.000006462717,0.02122579,0.003186284,0.01995055,0.5173992,0.01482048,0.4090788],"study_design_scores_gemma":[0.0012996,5.388379e-7,0.04110268,0.002011239,0.0002980437,0.000009369795,0.0005532397,0.5272571,0.08788823,0.009832012,0.3273815,0.002366437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02911492,0.001902913,0.747903,0.001933401,0.0009403462,0.0007159275,0.00006933674,0.000998792,0.2164213],"genre_scores_gemma":[0.9581248,0.0008028847,0.03688676,0.00006742254,0.00004618373,0.0001057613,0.0009178729,0.00007657506,0.002971702],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9290099,"threshold_uncertainty_score":0.999831,"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."}}