{"id":"W2973073913","doi":"10.1002/ece3.5627","title":"How do trees respond to species mixing in experimental compared to observational studies?","year":2019,"lang":"en","type":"article","venue":"Ecology and Evolution","topic":"Ecology and Vegetation Dynamics Studies","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Deutsches Zentrum für integrative Biodiversitätsforschung Halle-Jena-Leipzig; Helmholtz-Zentrum für Umweltforschung; Deutsche Forschungsgemeinschaft; European Commission","keywords":"Biodiversity; Tree (set theory); Ecology; Species diversity; Productivity; Mixing (physics); Biology; Plant species; Consistency (knowledge bases); Mathematics; Computer science; Artificial intelligence","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":[0.03296022,0.0006691071,0.0009214656,0.001306768,0.0009261029,0.001734604,0.001414899,0.001101622,0.001206586],"category_scores_gemma":[0.09846342,0.0005046023,0.0008643471,0.001180862,0.003376069,0.002693924,0.002394683,0.000782165,0.0002219736],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005151805,"about_ca_system_score_gemma":0.0002835101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001357276,"about_ca_topic_score_gemma":0.002580817,"domain_scores_codex":[0.9557899,0.03011809,0.003168895,0.006176224,0.004151391,0.0005954893],"domain_scores_gemma":[0.871561,0.08535776,0.02612259,0.01370818,0.002267529,0.0009829215],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002530602,0.001142753,0.8897498,0.001499444,0.003983307,0.0001525749,0.004205602,0.001887888,0.04161666,0.002406963,0.0005127444,0.05031163],"study_design_scores_gemma":[0.00007925564,0.001508072,0.9770354,0.0001855964,0.000603048,0.0001888056,0.001650098,0.002079932,0.008315304,0.005812466,0.002470751,0.00007140893],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9623627,0.004178108,0.02841737,0.0005755905,0.0001733279,0.0002130575,0.0004495635,0.00004674596,0.003583512],"genre_scores_gemma":[0.9920139,0.0005949395,0.005779551,0.0005683634,0.0001341007,0.0003046579,0.0003705416,0.00002233575,0.0002116496],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03296022,"threshold_uncertainty_score":0.1743124,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04004365406304421,"score_gpt":0.2834952160415437,"score_spread":0.2434515619784995,"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."}}