{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002608078,0.0004880518,0.0005020995,0.0003010549,0.0002552273,0.0004784352,0.0008244495,0.0006112162,0.003274577],"category_scores_gemma":[0.0006522354,0.0002256346,0.0003686462,0.0004022513,0.0002424479,0.0006582823,0.0002456588,0.0004232884,0.0003000906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001043601,"about_ca_system_score_gemma":0.0007048845,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01184544,"about_ca_topic_score_gemma":0.008275417,"domain_scores_codex":[0.9998578,0.00004074699,0.000006270422,0.00004327272,0.00003301529,0.00001883326],"domain_scores_gemma":[0.9997875,0.0001160386,0.00002096396,0.00002029657,0.00004748339,0.000007785307],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002236563,0.00002066111,0.0008595026,0.00001456888,0.00000622128,0.00001528808,0.000008952177,0.9961495,0.0009230561,0.0004833174,0.00008903541,0.001407463],"study_design_scores_gemma":[0.000007720424,0.00002320792,0.0008497531,0.000002498002,0.00000466408,0.000007331032,0.00001284962,0.9976602,0.0007641359,0.0003107173,0.0003529582,0.000004025434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7720417,0.0001693454,0.2023069,0.0001230177,0.00003545768,0.0002272142,0.001807449,0.0006699453,0.02261895],"genre_scores_gemma":[0.9856917,0.00007791277,0.009198005,0.00002032205,0.000002952716,0.0001610249,0.0005272398,0.00005765636,0.004263242],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01184544,"threshold_uncertainty_score":0.02355295,"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."}}