{"id":"W2294604582","doi":"10.1007/978-3-662-48971-0_8","title":"Multidimensional Range Selection","year":2015,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Range (aeronautics); Selection (genetic algorithm); Artificial intelligence; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009258501,0.0004682368,0.0004345578,0.0006655047,0.0002711295,0.0003163138,0.002244663,0.0003240976,0.00004873569],"category_scores_gemma":[0.00007293012,0.0003999188,0.0000974775,0.0005304706,0.000342558,0.0009509372,0.001880815,0.00076333,0.0001556745],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003689756,"about_ca_system_score_gemma":0.0007438547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004097657,"about_ca_topic_score_gemma":0.00003481809,"domain_scores_codex":[0.9960748,0.0000353165,0.0004138111,0.001497938,0.001412714,0.0005654321],"domain_scores_gemma":[0.9977552,0.0002254229,0.0002300002,0.001073285,0.000456757,0.0002593354],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000108225,0.00004523529,0.00004656247,0.00001770598,0.000009299868,0.00008555012,0.0002797013,0.02489891,0.0001920769,0.02685479,0.0008017906,0.9467576],"study_design_scores_gemma":[0.0003881198,0.0001564732,0.0001051825,0.0002281466,0.000005127645,0.0001144414,4.156907e-8,0.8915958,0.0003711805,0.08690995,0.01952476,0.0006007546],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00002730309,0.0006985434,0.9938556,0.0003478238,0.00219967,0.0002735626,0.000007639027,0.0002136783,0.002376196],"genre_scores_gemma":[0.01153628,0.00003596945,0.985484,0.0008269441,0.0009252835,0.000008900489,0.00001852759,0.00003871431,0.001125406],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9461568,"threshold_uncertainty_score":0.9998453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02595885077230979,"score_gpt":0.2605602080489983,"score_spread":0.2346013572766885,"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."}}