{"id":"W2799463367","doi":"10.1111/jbi.13242","title":"Building up biogeography: Pattern to process","year":2018,"lang":"en","type":"article","venue":"Journal of Biogeography","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; University of British Columbia","funders":"Center for Makroøkologi, Evolution og Klima; Deutsche Forschungsgemeinschaft; Fonds de recherche du Québec – Nature et technologies; Fundação para a Ciência e a Tecnologia; Villum Fonden; National Science Foundation","keywords":"Biogeography; Ecology; Data science; Geography; Computer science; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.004945986,0.0006733259,0.001025898,0.00268428,0.001137559,0.007453906,0.001325016,0.001808014,0.008448402],"category_scores_gemma":[0.0100319,0.0004967663,0.000942211,0.002497639,0.01713985,0.0169937,0.00463659,0.004769013,0.0007640298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002389413,"about_ca_system_score_gemma":0.001880061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004044807,"about_ca_topic_score_gemma":0.002521843,"domain_scores_codex":[0.9977282,0.001243488,0.0001224484,0.0005495779,0.0002674082,0.00008884993],"domain_scores_gemma":[0.993597,0.004131211,0.0004320328,0.001047594,0.0004871323,0.0003049257],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001639255,0.0000106866,0.002095721,0.00034814,0.00008403639,0.00006232334,0.0009701659,0.005433018,0.0003634981,0.9513732,0.002839355,0.03640358],"study_design_scores_gemma":[0.000003300797,0.000008580399,0.0009900715,0.0001501638,0.00001707722,0.00003229536,0.0002468434,0.003736038,0.0001440226,0.9751604,0.01949739,0.00001369607],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02514056,0.03280586,0.8431091,0.0633371,0.001476435,0.00009276206,0.0003807879,0.0005644499,0.03309284],"genre_scores_gemma":[0.7239252,0.02871342,0.2332021,0.005652311,0.001741456,0.0002146259,0.0003701937,0.0004516522,0.005729134],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008448402,"threshold_uncertainty_score":0.02826267,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0168471754216948,"score_gpt":0.2756108268872354,"score_spread":0.2587636514655406,"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."}}