{"id":"W2069797356","doi":"10.6000/1929-6002.2013.02.01.9","title":"Logistic Cost Analysis of Rice Straw to Optimize Power Plant in Malaysia","year":2013,"lang":"en","type":"article","venue":"Journal of Technology Innovations in Renewable Energy","topic":"Forest Biomass Utilization and Management","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universiti Malaya","keywords":"Rice straw; Truck; Straw; Agricultural engineering; Total cost; Cost analysis; Logistic regression; Environmental science; Operations management; Waste management; Engineering; Business; Operations research; Mathematics; Statistics; Automotive engineering; Agronomy; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006004445,0.000743293,0.000401226,0.0009295426,0.0003374098,0.0008962256,0.0004982513,0.0004662617,0.002373815],"category_scores_gemma":[0.001255764,0.0004820371,0.0008603255,0.00097693,0.000300417,0.001016324,0.0005133449,0.0005503664,0.0001864479],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002518522,"about_ca_system_score_gemma":0.002089598,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0193087,"about_ca_topic_score_gemma":0.01581691,"domain_scores_codex":[0.9997286,0.0001104159,0.000009043295,0.00002634083,0.000068227,0.00005732477],"domain_scores_gemma":[0.9995311,0.0003021337,0.00005393639,0.00001607361,0.00007510375,0.00002162503],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00005763501,0.00001932375,0.001670217,0.00004117536,0.00001319498,0.00007874017,0.00001045879,0.9909121,0.0004894012,0.001371536,0.0001604289,0.005175772],"study_design_scores_gemma":[0.000006734766,0.0000806507,0.001787411,0.00001020757,0.00001491345,0.00003045977,0.00006203048,0.9954325,0.001116683,0.0009387852,0.0005093403,0.00001018044],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8484266,0.0008522306,0.1307678,0.0005498661,0.00002813919,0.0001634396,0.0006835064,0.0001265042,0.01840186],"genre_scores_gemma":[0.9902775,0.0002513844,0.006544481,0.000008558852,0.000002313065,0.00004094614,0.0001527136,0.00001909584,0.002702866],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0193087,"threshold_uncertainty_score":0.0383926,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01205972091703329,"score_gpt":0.233938475514174,"score_spread":0.2218787545971408,"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."}}