{"id":"W2135543697","doi":"10.1002/aic.14113","title":"Inventory pinch algorithm for gasoline blend planning","year":2013,"lang":"en","type":"article","venue":"AIChE Journal","topic":"Process Optimization and Integration","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Ontario Research Foundation","keywords":"Schedule; Computer science; Scheduling (production processes); Recipe; Gasoline; Algorithm; Grid; Mathematical optimization; Mathematics; Engineering; Chemistry; Waste management","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0001010786,0.00007277261,0.00007545504,0.00007141768,0.00007264108,0.00008123848,0.00007635153,0.00004712561,0.0003084339],"category_scores_gemma":[0.00002904638,0.00006363732,0.0000359445,0.00005903024,0.000007158985,0.0003595598,0.000005270612,0.0001635327,0.00003837856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004147808,"about_ca_system_score_gemma":0.00001865572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001723862,"about_ca_topic_score_gemma":4.720342e-7,"domain_scores_codex":[0.9995515,0.000006730737,0.0001706063,0.0000474341,0.00007948003,0.000144237],"domain_scores_gemma":[0.9997049,0.00001401084,0.00003549805,0.00004610396,0.0001212769,0.00007821597],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006457697,0.00005097961,0.0005311312,0.00008028592,0.0001444817,0.000004336513,0.001101601,0.2244941,0.005592908,0.0001967761,0.4883561,0.2794409],"study_design_scores_gemma":[0.0003040177,0.0000300661,0.0001337226,0.00003087227,0.000007674145,0.00003942073,0.00009624938,0.9773762,0.002028836,0.0005671787,0.01929603,0.00008979197],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004558107,0.0008233251,0.9908284,0.0001858224,0.0004689597,0.0001018759,0.000002287035,0.0000942258,0.002937006],"genre_scores_gemma":[0.5388525,0.00112271,0.4484815,0.00182806,0.00386771,0.0001672636,0.0001028659,0.0001772114,0.005400165],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7528821,"threshold_uncertainty_score":0.3377135,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01380297273651435,"score_gpt":0.2412657487165615,"score_spread":0.2274627759800471,"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."}}