{"id":"W4402099505","doi":"10.1093/biosci/biae070","title":"Going global by going local: Impacts and opportunities of geographically focused data integration","year":2024,"lang":"en","type":"article","venue":"BioScience","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Commonwealth Scientific and Industrial Research Organisation; Australian Government","keywords":"Geography; Regional science; Environmental resource management; Earth science; Economic geography; Environmental planning; Environmental science; Geology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02783018,0.0004054325,0.0004016399,0.003747574,0.003848085,0.01296276,0.002277995,0.001620579,0.008491761],"category_scores_gemma":[0.06306211,0.0003356628,0.0007293966,0.008807787,0.00588231,0.01744915,0.01419109,0.002548572,0.000782621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00261082,"about_ca_system_score_gemma":0.004180094,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01218262,"about_ca_topic_score_gemma":0.01458467,"domain_scores_codex":[0.9717543,0.01948547,0.0007863203,0.001798094,0.004345293,0.001830518],"domain_scores_gemma":[0.9253146,0.05027113,0.004655645,0.008785182,0.008168777,0.002804621],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0003715424,0.000473026,0.2274142,0.001346506,0.0004212506,0.001606045,0.03240426,0.01795857,0.002618994,0.388674,0.02561598,0.3010956],"study_design_scores_gemma":[0.00005638369,0.0005653857,0.1362241,0.004393675,0.0004985012,0.002042568,0.2214084,0.02583344,0.004520255,0.2828913,0.3213556,0.0002104014],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.615702,0.006706941,0.05934459,0.09107232,0.0006141484,0.0004324527,0.002432903,0.0005225317,0.2231721],"genre_scores_gemma":[0.9741769,0.001903107,0.01882795,0.002148137,0.0001782714,0.00008588868,0.0007877869,0.0001461356,0.001745783],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02783018,"threshold_uncertainty_score":0.1471818,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08129770475115426,"score_gpt":0.3411048621966541,"score_spread":0.2598071574454999,"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."}}