{"id":"W4320916047","doi":"10.1007/978-3-031-21983-2_11","title":"Industrial Composition, Remote Working and Mobility Changes in Canada and the US During the COVID-19 Pandemic: A SHAP Value Analysis of XGBoost Predictions","year":2023,"lang":"en","type":"book-chapter","venue":"Footprints of regional science","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Accommodation; Government (linguistics); Coronavirus disease 2019 (COVID-19); Pandemic; Value (mathematics); Business; Demographic economics; Geography; Computer science; Economics; Medicine; Psychology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.003009364,0.000140872,0.0003969681,0.0002480522,0.0007915695,0.00004100686,0.0006238566,0.0001197298,0.00002587214],"category_scores_gemma":[0.0003022977,0.00009474188,0.00007910526,0.0009996559,0.005544088,0.00007318595,0.0001673896,0.0003250707,8.788982e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005349234,"about_ca_system_score_gemma":0.00243253,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8612143,"about_ca_topic_score_gemma":0.983596,"domain_scores_codex":[0.9977914,0.0001089899,0.0003922414,0.0004674001,0.001000074,0.0002398733],"domain_scores_gemma":[0.998202,0.0008012558,0.0003920155,0.0002980483,0.00015013,0.0001566034],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001317496,0.00001067877,0.9806255,0.00003394179,0.0001294438,0.000003535114,0.002375866,0.0005509175,0.00002413061,0.0151497,0.0000136297,0.0009509834],"study_design_scores_gemma":[0.0004835425,0.00001012903,0.9885868,0.0001412986,0.0002690757,0.000001596658,0.0005021263,0.0004378189,0.000005127994,0.007340111,0.00206729,0.00015504],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9923025,0.0001682263,0.00001641713,0.002769976,0.0001509473,0.0005629183,0.0000862213,0.00001773552,0.003925022],"genre_scores_gemma":[0.99799,0.0004175742,0.00001163091,0.00009040192,0.00007380256,0.000006171148,0.000005767562,0.000005106838,0.001399583],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1223817,"threshold_uncertainty_score":0.9971622,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09664043427002378,"score_gpt":0.3057199757480418,"score_spread":0.209079541478018,"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."}}