{"id":"W3123491167","doi":"10.1101/2021.01.15.426694","title":"Alternative futures for global biological invasions","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Ecosystem dynamics and resilience","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Comisión Nacional de Investigación Científica y Tecnológica; Deutsches Zentrum für integrative Biodiversitätsforschung Halle-Jena-Leipzig; Agencia Estatal de Investigación; Austrian Science Fund; Bundesministerium für Bildung und Forschung; Deutsche Forschungsgemeinschaft; Sight Research UK; Natural Environment Research Council; Biodiversa+; National Science Foundation","keywords":"Futures contract; Sustainability; Biodiversity; Environmental resource management; Scenario analysis; Climate change; Set (abstract data type); Scenario planning; Citizen journalism; Ecology; Geography; Political science; Business; Computer science; Environmental science; Biology","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"],"consensus_categories":[],"category_scores_codex":[0.0003905762,0.0004312817,0.0004610097,0.0000346332,0.0002921125,0.0002092446,0.0008516526,0.0004963239,0.0001970522],"category_scores_gemma":[0.0002270585,0.0003751136,0.000237362,0.0003153025,0.0002121701,0.0001080365,0.001449727,0.0003688673,0.00006811173],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005813797,"about_ca_system_score_gemma":0.0001523905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002525403,"about_ca_topic_score_gemma":0.0001296092,"domain_scores_codex":[0.9973921,0.0001191723,0.0004093111,0.001196576,0.0003363185,0.0005465563],"domain_scores_gemma":[0.9984296,0.00006703602,0.0002976013,0.0008379084,0.00008881155,0.000279031],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00009398301,0.001033183,0.3771731,0.0005979278,0.0004711003,0.0003657112,0.00006192082,0.01279608,0.5903654,0.01520713,0.001808758,0.00002571715],"study_design_scores_gemma":[0.001307301,0.0002915786,0.8873628,0.0008685072,0.0001845135,1.209952e-7,0.00006581474,0.02806749,0.06986383,0.0001651158,0.008834101,0.002988776],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9847509,0.0004655847,0.01146499,0.0001431942,0.001639998,0.0008610917,0.0004547424,0.0001436783,0.00007583216],"genre_scores_gemma":[0.9875665,0.0002565406,0.01134198,0.0002294028,0.0002858874,0.0002783751,8.561207e-7,0.0000360131,0.000004397558],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5205016,"threshold_uncertainty_score":0.9998701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02311482769296054,"score_gpt":0.2303006602292374,"score_spread":0.2071858325362768,"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."}}