{"id":"W3018641497","doi":"10.1111/oik.07195","title":"Evaluating <i>Sphagnum</i> traits in the context of resource economics and optimal partitioning theories","year":2020,"lang":"en","type":"article","venue":"Oikos","topic":"Peatlands and Wetlands Ecology","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Sphagnum; Biology; Trait; Biomass (ecology); Context (archaeology); Moss; Ecology; Biomass partitioning; Vascular plant; Resource Acquisition Is Initialization; Resource (disambiguation); Resource allocation; Peat; Species richness","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":[0.001454291,0.0004193136,0.0002982867,0.000657533,0.0003034191,0.0007690053,0.0003319109,0.0002934117,0.0004673917],"category_scores_gemma":[0.001954303,0.0001245842,0.0002643001,0.0004092404,0.0007896809,0.0008460802,0.0005699361,0.0003967451,0.00004677247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008113578,"about_ca_system_score_gemma":0.0004012909,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002408483,"about_ca_topic_score_gemma":0.003412352,"domain_scores_codex":[0.9995905,0.0001841119,0.0000296727,0.00007823704,0.00008138369,0.00003609775],"domain_scores_gemma":[0.998444,0.0007907086,0.0003977602,0.0001276773,0.0001183147,0.0001214857],"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":[0.001176978,0.0003026619,0.3659032,0.0003860207,0.0003860895,0.0004120223,0.0006346725,0.0475569,0.5305465,0.01212247,0.0001527004,0.04041971],"study_design_scores_gemma":[0.00003590173,0.000883346,0.8279625,0.00003347192,0.0001241466,0.0003393674,0.0008652903,0.08730222,0.06389552,0.01758747,0.0009026258,0.00006812218],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9942673,0.0001016934,0.004880201,0.00002941931,0.000001706651,0.000009104606,0.00004531136,0.000007016639,0.0006584119],"genre_scores_gemma":[0.9974009,0.00003143489,0.002424771,0.00001477996,0.000001328946,0.000009156656,0.00003948483,0.000004242821,0.00007399142],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002408483,"threshold_uncertainty_score":0.007691145,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02232400163724962,"score_gpt":0.2520144146422704,"score_spread":0.2296904130050207,"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."}}