{"id":"W2616650836","doi":"10.1111/ddi.12556","title":"Multifaceted biodiversity hotspots of marine mammals for conservation priorities","year":2017,"lang":"en","type":"article","venue":"Diversity and Distributions","topic":"Marine animal studies overview","field":"Environmental Science","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies; Agence Nationale de la Recherche","keywords":"Species richness; Biodiversity; Biodiversity hotspot; Ecology; Mammal; Phylogenetic diversity; Endangered species; Geography; Trait; Biology; Species diversity; Spatial ecology; Marine mammal; Ecosystem; Phylogenetic tree; Habitat","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006257214,0.000218952,0.0002894665,0.002510501,0.0003200632,0.0007986519,0.0002928489,0.0001912979,0.002450512],"category_scores_gemma":[0.001392572,0.0001335554,0.0003174038,0.001787737,0.0004002288,0.0005364456,0.001329169,0.0002359114,0.0001362135],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002448925,"about_ca_system_score_gemma":0.0002466389,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002591863,"about_ca_topic_score_gemma":0.007137056,"domain_scores_codex":[0.999488,0.0001568102,0.00003858771,0.0001391747,0.00007898128,0.00009834274],"domain_scores_gemma":[0.9982377,0.0003844915,0.0009078457,0.0001229046,0.0001453685,0.0002015619],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005101613,0.00001068716,0.9864126,0.0001249459,0.0001834611,0.000120357,0.0004730309,0.0004065137,0.00228742,0.0003729547,0.0001796552,0.009377171],"study_design_scores_gemma":[9.628253e-7,0.0000144691,0.9982722,0.0000172063,0.00002245543,0.00008583553,0.0005751146,0.0004292849,0.00008771047,0.0002085614,0.0002827761,0.000003448737],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.996914,0.000581934,0.0008409203,0.00008000724,0.000003682913,0.000009660232,0.0005656542,0.00001360164,0.0009903861],"genre_scores_gemma":[0.9991488,0.00008110164,0.0004584518,0.000007907297,0.000005016391,0.000004620012,0.0002334298,0.000001251982,0.00005931939],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002591863,"threshold_uncertainty_score":0.008197844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04579556806848433,"score_gpt":0.248428043108866,"score_spread":0.2026324750403816,"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."}}