{"id":"W4315432227","doi":"10.5281/zenodo.7517506","title":"Artifact of \"To Pack or Not to Pack: A Generalized Packing Analysis and Transformation\"","year":2023,"lang":"en","type":"paratext","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Artifact (error); Transformation (genetics); Computer science; Artificial intelligence; Engineering drawing; Computer vision; Computer graphics (images); 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","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003101495,0.0002524876,0.0004242204,0.0009773159,0.0005054218,0.0003544263,0.0004148326,0.0001316024,0.005881424],"category_scores_gemma":[0.0003269617,0.0002522471,0.00008131367,0.001496074,0.00005182221,0.0001346388,0.0002733872,0.0002463681,0.007863813],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001311885,"about_ca_system_score_gemma":0.000003811341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002070718,"about_ca_topic_score_gemma":0.000003621773,"domain_scores_codex":[0.9984064,0.0001071293,0.0004438377,0.0003650195,0.0003238217,0.0003538516],"domain_scores_gemma":[0.9989243,0.00004410278,0.00009439374,0.0003947865,0.0003120454,0.0002303364],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001399769,0.00001919647,1.351385e-7,0.0003906581,0.0003805772,0.000004096612,0.002299165,0.801726,0.001120032,0.0001434447,0.1652802,0.0284965],"study_design_scores_gemma":[0.0005683452,0.0002148172,0.0001968444,0.00013051,0.0002514201,0.00001023405,0.0002629957,0.02292042,0.005662318,0.00003686762,0.9691724,0.0005728374],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006228546,0.00005609672,0.94863,0.000315863,0.0003601889,0.001044506,0.002314806,0.001284285,0.03976569],"genre_scores_gemma":[0.9078238,0.003413108,0.01665068,0.0003648587,0.0005700935,0.000001271255,0.02440181,0.007442948,0.03933145],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9319794,"threshold_uncertainty_score":0.999993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04424918884998325,"score_gpt":0.2783173997371487,"score_spread":0.2340682108871655,"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."}}