{"id":"W2150724809","doi":"10.4067/s0718-221x2009000100003","title":"MODEL TO ASSESS ENERGY CONSUMPTION IN INDUSTRIAL LUMBER KILNS","year":2009,"lang":"es","type":"article","venue":"Americanae (AECID Library)","topic":"Greenhouse Technology and Climate Control","field":"Agricultural and Biological Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"FPInnovations","funders":"","keywords":"Kiln; Energy consumption; Environmental science; Work (physics); Calibration; Energy (signal processing); Waste management; Efficient energy use; Engineering; Process engineering; Mechanical engineering; Mathematics; Statistics; Electrical 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001029872,0.0003555495,0.0005510019,0.0001218055,0.000153382,0.00009064942,0.0006754832,0.0004815751,0.001493156],"category_scores_gemma":[0.00005461438,0.0001999824,0.0001443582,0.001133421,0.0002063412,0.0006198405,0.0001540223,0.0004068087,0.0001728016],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004372539,"about_ca_system_score_gemma":0.00004567728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004882207,"about_ca_topic_score_gemma":0.0003591601,"domain_scores_codex":[0.9977323,0.0001646165,0.0004824444,0.0006622991,0.000239238,0.0007190414],"domain_scores_gemma":[0.9991703,0.0001411405,0.0001931467,0.0002041423,0.00001989595,0.0002713544],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002011251,0.000374074,0.6559648,0.00000313082,0.00003033476,0.00005729493,0.00003638258,0.0002172691,0.01543926,0.02124386,0.01744875,0.2889837],"study_design_scores_gemma":[0.0007289759,0.0009822636,0.9832811,0.00008936547,0.00003769623,0.000005144771,0.0001312015,0.002505822,0.001556582,0.002090181,0.007938822,0.0006528809],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9739282,0.0002261505,0.0000544045,0.02231106,0.0001519241,0.0002621361,0.00007684797,0.0003099184,0.002679428],"genre_scores_gemma":[0.9902338,0.0006770512,0.0002254634,0.007490225,0.0002787696,0.00003462298,0.00006522157,0.000006248932,0.000988613],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3273163,"threshold_uncertainty_score":0.9994196,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06076786329989065,"score_gpt":0.2542486919780155,"score_spread":0.1934808286781248,"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."}}