{"id":"W7020986328","doi":"","title":"A novel stope layout optimization based on robust genetic algorithms","year":2018,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Microstructure and mechanical properties","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Goldcorp; Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Genetic algorithm; Minification; Optimization problem; Work (physics); Process (computing); Robust optimization","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0007106423,0.0009175242,0.000803143,0.000337203,0.001139722,0.0002524006,0.001011501,0.001035179,0.003237969],"category_scores_gemma":[0.0006217741,0.0008186875,0.0002933664,0.0003996813,0.00009811647,0.0004384643,0.0001250344,0.0008834722,0.0009586285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004623412,"about_ca_system_score_gemma":0.0001282351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002581252,"about_ca_topic_score_gemma":0.0003763962,"domain_scores_codex":[0.9953173,0.0002558549,0.0009603312,0.00157026,0.001032886,0.0008633269],"domain_scores_gemma":[0.997166,0.0001045462,0.0006365895,0.001086556,0.0006447611,0.0003615236],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006141593,0.0002835138,5.545937e-7,0.0003095011,0.00004357253,0.00002758877,0.00001305944,0.04687753,0.9291713,0.001775342,0.00003314033,0.02085078],"study_design_scores_gemma":[0.001861186,0.0009103992,0.00006095601,0.0008603797,0.0003079288,0.00002991279,0.0001265852,0.01988084,0.9512945,0.001288136,0.02146682,0.001912307],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9128449,0.0003839526,0.0007051927,0.00004224593,0.01092128,0.002738868,0.00486931,0.001045369,0.06644889],"genre_scores_gemma":[0.8171226,0.000102083,0.1595495,0.002355759,0.0005474641,0.0003306922,0.002923532,0.0006856262,0.01638274],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1588443,"threshold_uncertainty_score":0.9998192,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02278756037600965,"score_gpt":0.232343900875622,"score_spread":0.2095563404996124,"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."}}