{"id":"W4399336805","doi":"10.2139/ssrn.4853507","title":"Bike-Share Ridership Prediction for Network Expansion Using Graph Neural Networks","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Advanced Optical Network Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"","keywords":"Artificial neural network; Computer science; Graph; Transport engineering; Artificial intelligence; Engineering; Theoretical computer science","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","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0009291144,0.0006454554,0.0005770673,0.0003004338,0.0002861754,0.0002196434,0.0005995603,0.0009581188,0.000008746082],"category_scores_gemma":[0.00007890482,0.0006302376,0.0004993523,0.0004925038,0.00008355174,0.0001653404,0.0004478567,0.01129129,0.000003646428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001950851,"about_ca_system_score_gemma":0.0004006691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004411052,"about_ca_topic_score_gemma":0.0001070662,"domain_scores_codex":[0.994288,0.00004923114,0.000719959,0.0005639042,0.0003034078,0.004075547],"domain_scores_gemma":[0.9989669,0.0001475357,0.0001756589,0.0004629242,0.0001179641,0.000129068],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004254046,0.000008223132,0.00005183302,0.0001597291,0.0003589945,0.00000577773,0.00001600707,0.9611057,0.00002868122,0.00740653,0.0009019519,0.02991402],"study_design_scores_gemma":[0.0001657377,0.0001144827,0.000007488372,0.0003774537,0.0001630141,0.0001149552,0.0001235084,0.5805867,0.000007977523,0.4178204,0.0001953921,0.0003228694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04635832,0.08453317,0.8594543,0.0002038816,0.006260028,0.0008250297,0.00004455372,0.002238741,0.00008196356],"genre_scores_gemma":[0.9588157,0.0228264,0.0113349,0.00003112254,0.006245876,0.0001348093,0.0001314169,0.0003810334,0.00009873854],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9124574,"threshold_uncertainty_score":0.9996149,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01855880193079002,"score_gpt":0.2465563641838067,"score_spread":0.2279975622530167,"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."}}