{"id":"W7092196422","doi":"10.5281/zenodo.17369514","title":"Investigating Corporate Editors in OpenStreetMap","year":2025,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Research Data Management Practices","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of the Environment, Conservation and Parks; University of Toronto; Carleton University","funders":"","keywords":"Context (archaeology); Field (mathematics); Government (linguistics); Information system","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":["scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002155692,0.0001171767,0.0001231466,0.0006257388,0.001133603,0.008006797,0.004605256,0.00004247624,0.0003524065],"category_scores_gemma":[0.003000078,0.000126697,0.00002424024,0.002178741,0.0001498821,0.01088659,0.006429831,0.0003597905,0.001389471],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001550639,"about_ca_system_score_gemma":0.00001334634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005218566,"about_ca_topic_score_gemma":0.00000150313,"domain_scores_codex":[0.9976572,0.0006032343,0.0002838746,0.0005762826,0.0004667422,0.000412699],"domain_scores_gemma":[0.9983625,0.00007957098,0.0001609848,0.0009381931,0.0003268062,0.0001319698],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001223297,0.0001416552,0.0001334077,0.0001045657,0.00003712184,0.00004889966,0.0003822853,0.0001863498,0.001972448,0.5052307,0.3844548,0.1072955],"study_design_scores_gemma":[0.0003850748,0.00006667195,0.002490141,0.00006564635,0.000002888439,0.000007543614,0.0001529534,0.004001543,0.0003388933,0.002805665,0.9895507,0.0001323469],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.01314344,0.00009988141,0.4400073,0.01672791,0.001083812,0.001264608,0.00005642169,0.00170673,0.5259098],"genre_scores_gemma":[0.9578331,0.0004007595,0.02183009,0.001625336,0.0007554318,4.333514e-7,0.001259794,0.001195864,0.01509918],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9446897,"threshold_uncertainty_score":0.999388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1017714980949156,"score_gpt":0.3050922046647879,"score_spread":0.2033207065698724,"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."}}