{"id":"W2899163791","doi":"10.1101/457424","title":"Biodiversity trends are stronger in marine than terrestrial assemblages","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke; University of British Columbia; McGill University; Memorial University of Newfoundland","funders":"Deutsche Forschungsgemeinschaft; Leverhulme Trust; Deutsches Zentrum für integrative Biodiversitätsforschung Halle-Jena-Leipzig; National Science Foundation","keywords":"Biodiversity; Species richness; Extinction (optical mineralogy); Ecology; Biome; Geography; Climate change; Global change; Taxon; Global biodiversity; Biology; Ecosystem","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":[0.0006784264,0.0002147882,0.0002859704,0.001217509,0.0003160383,0.0009483561,0.0002092335,0.0001819546,0.003516778],"category_scores_gemma":[0.002279646,0.0001583156,0.0003886772,0.00135759,0.00049771,0.0007182036,0.0006218277,0.0003349977,0.0004995184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002022341,"about_ca_system_score_gemma":0.0001516199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005034585,"about_ca_topic_score_gemma":0.006234079,"domain_scores_codex":[0.9996266,0.00006135914,0.00003161463,0.0001734042,0.00006668804,0.00004028197],"domain_scores_gemma":[0.9985214,0.0003599525,0.0006267413,0.0001579381,0.000205184,0.0001288712],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003958842,0.00001066736,0.981555,0.00008770151,0.0003074809,0.00003056338,0.0001771297,0.00143591,0.006821868,0.0002371222,0.0006735206,0.008623485],"study_design_scores_gemma":[0.000001817681,0.00001288973,0.9957396,0.00001199792,0.00003412193,0.00005242731,0.0001335846,0.001700954,0.0005824718,0.0003213471,0.001403886,0.000004977296],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9920018,0.0005176268,0.001808008,0.0001997301,0.00001664004,0.00000540825,0.00276646,0.00009490575,0.00258947],"genre_scores_gemma":[0.9977222,0.0001156015,0.0005846705,0.00004607728,0.00001513562,0.00000355196,0.001239823,0.00001638236,0.000256527],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005034585,"threshold_uncertainty_score":0.01176476,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0257042670744312,"score_gpt":0.226208974606167,"score_spread":0.2005047075317358,"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."}}