{"id":"W2163496505","doi":"10.1093/icesjms/fsn118","title":"Average functional distinctness as a measure of the composition of assemblages","year":2008,"lang":"en","type":"article","venue":"ICES Journal of Marine Science","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":108,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Environment Research Council; Sight Research UK; Department for Environment, Food and Rural Affairs, UK Government; Government of the United Kingdom; Centre for Environment, Fisheries and Aquaculture Science","keywords":"Species richness; Biodiversity; Ecosystem; Ecology; Functional diversity; Diversity index; Taxonomic rank; Marine ecosystem; Global biodiversity; Phylogenetic diversity; Fish <Actinopterygii>; Species diversity; Index (typography); Geography; Biology; Phylogenetic tree; Fishery; Computer science; Taxon","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":[0.001958672,0.0003908288,0.0004045221,0.005316109,0.0004439496,0.001005914,0.0004016868,0.0004397937,0.003059812],"category_scores_gemma":[0.005752464,0.0001782708,0.0004076535,0.002789531,0.0008786434,0.001780268,0.0008747885,0.0006055021,0.0004048024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005754664,"about_ca_system_score_gemma":0.0001612207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001493128,"about_ca_topic_score_gemma":0.001971216,"domain_scores_codex":[0.9989303,0.0002665226,0.0001305561,0.0002354814,0.0003851647,0.0000520326],"domain_scores_gemma":[0.9955488,0.001638485,0.001267506,0.0005226398,0.0006741248,0.0003484263],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005119537,0.0001635474,0.808061,0.0003112737,0.0007845465,0.0002490769,0.0009817495,0.006873238,0.04453721,0.01124792,0.002843782,0.1234346],"study_design_scores_gemma":[0.00001440553,0.0002205472,0.9519301,0.00005799341,0.00006947817,0.0006256757,0.0005716369,0.02292223,0.004624038,0.01548757,0.003418308,0.000058039],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.94494,0.001238733,0.0442231,0.0001562288,0.00006266469,0.00005874106,0.00165548,0.0001322043,0.007532991],"genre_scores_gemma":[0.9848523,0.0001128368,0.01337089,0.00002438726,0.00003153605,0.00002748271,0.0008109099,0.00001751266,0.0007522232],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005316109,"threshold_uncertainty_score":0.01035857,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02142355700326737,"score_gpt":0.248036360949731,"score_spread":0.2266128039464636,"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."}}