{"id":"W4408428961","doi":"10.5194/egusphere-egu25-14621","title":"Advancing Equity in Geosciences: Insights and Actions from the Canadian EDI Landscape","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Geological Survey of Canada","funders":"","keywords":"Equity (law); Geography; Regional science; Environmental resource management; Economic geography; Political science; Economics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","open_science"],"consensus_categories":[],"category_scores_codex":[0.01659062,0.0007311385,0.0007373987,0.004706154,0.06375823,0.02775024,0.003691485,0.005514876,0.009772467],"category_scores_gemma":[0.02423559,0.0004412744,0.0006855185,0.01075063,0.02845328,0.0094508,0.02193091,0.01117344,0.0006088989],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.2056665,"about_ca_system_score_gemma":0.4666585,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9833167,"about_ca_topic_score_gemma":0.9930081,"domain_scores_codex":[0.979793,0.003563731,0.0003681594,0.0009291658,0.007264687,0.008081356],"domain_scores_gemma":[0.9717003,0.006181122,0.00075306,0.0007564343,0.008270525,0.01233856],"domain_codex":null,"domain_gemma":"incentives","domain_candidate":"incentives","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.00005372557,0.00008219646,0.01340843,0.0005550283,0.00003105029,0.0008059061,0.23133,0.0005780566,0.0008818086,0.4157986,0.1760959,0.1603794],"study_design_scores_gemma":[0.00001089412,0.00002103277,0.01368872,0.0007671948,0.00002481849,0.0001401828,0.3420944,0.0003942529,0.0003621583,0.03688474,0.605501,0.0001106916],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.0692201,0.01583174,0.003397351,0.6160816,0.001796093,0.0001772458,0.0006639953,0.0001391386,0.2926927],"genre_scores_gemma":[0.8448875,0.02537013,0.009609072,0.06381045,0.0003790218,0.0001765692,0.0006355756,0.0002095694,0.0549222],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9963085,"threshold_uncertainty_score":0.9213142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03119854040589799,"score_gpt":0.2827033195784551,"score_spread":0.2515047791725571,"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."}}