{"id":"W3081095766","doi":"10.48550/arxiv.2008.11705","title":"Towards A Personal Shopper's Dilemma: Time vs Cost","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Skyline; Order (exchange); Profit (economics); Set (abstract data type); Heuristic; Baseline (sea); Dilemma; Measure (data warehouse); Operations research; Data mining; Business; Microeconomics; Mathematics; Economics; Artificial intelligence","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001997319,0.000397261,0.0004000454,0.0002185987,0.0001641713,0.0004572973,0.003518618,0.000190727,0.0002449069],"category_scores_gemma":[0.00002857558,0.0004575143,0.0002808537,0.0006571308,0.0001079477,0.0008422666,0.007195745,0.0006034799,0.00144304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001776774,"about_ca_system_score_gemma":0.0002026427,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001070013,"about_ca_topic_score_gemma":0.000007443527,"domain_scores_codex":[0.9975559,0.00009734539,0.0001793247,0.001525689,0.0001906001,0.0004510999],"domain_scores_gemma":[0.9983317,0.0000394003,0.0001700328,0.001068792,0.0001006976,0.0002894142],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002347219,0.0006854419,0.002325621,0.0005796554,0.001334092,0.009325818,0.001740245,0.02101976,0.00007621456,0.7948928,0.1153997,0.05238595],"study_design_scores_gemma":[0.0005361941,0.00007160804,0.0008248754,0.0000611743,0.00009116632,0.000003882465,0.00003136667,0.9515364,0.00001786626,0.009131841,0.03702647,0.0006671626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0150039,0.00003705953,0.9621359,0.002640944,0.001027516,0.0006559049,0.0002147985,0.0008112852,0.01747275],"genre_scores_gemma":[0.9862444,0.0001075468,0.004293865,0.0007722797,0.0003200644,0.000002584822,0.0002323463,0.0000314901,0.007995428],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9712405,"threshold_uncertainty_score":0.9997877,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07958839085075976,"score_gpt":0.1888081669892435,"score_spread":0.1092197761384838,"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."}}