{"id":"W1530407535","doi":"10.1111/jvs.12294","title":"Describing, explaining and predicting community assembly: a convincing trait‐based case study","year":2015,"lang":"en","type":"article","venue":"Journal of Vegetation Science","topic":"Ecology and Vegetation Dynamics Studies","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Trait; Abiotic component; Gradient analysis; Ecology; Selection (genetic algorithm); Vegetation (pathology); Biology; Ordination; Computer science; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":[0.004753286,0.0005692638,0.0005421586,0.001079529,0.001891203,0.001923981,0.001386886,0.002585544,0.001862366],"category_scores_gemma":[0.01438478,0.0003126832,0.001164826,0.001451856,0.001747106,0.002280748,0.001349819,0.00163989,0.0003319049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001511204,"about_ca_system_score_gemma":0.0006885533,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.017655,"about_ca_topic_score_gemma":0.01985415,"domain_scores_codex":[0.9984356,0.001023425,0.00005271414,0.0002263324,0.0001481686,0.0001137341],"domain_scores_gemma":[0.9814243,0.01550702,0.0005383993,0.00153449,0.0006201706,0.0003756393],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001403528,0.002337382,0.486632,0.0009040682,0.0007601733,0.01163846,0.01226547,0.254696,0.01347056,0.06625219,0.01366975,0.1359704],"study_design_scores_gemma":[0.0002640775,0.0004878079,0.07451914,0.0001343339,0.0002108739,0.002427696,0.004443709,0.8245258,0.0060681,0.07422683,0.01249899,0.0001927163],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.941485,0.0008033092,0.05080758,0.002258048,0.00002576553,0.0001015525,0.0006920099,0.0001705747,0.00365615],"genre_scores_gemma":[0.9506572,0.0001972134,0.04745124,0.000170315,0.00003021368,0.00005482799,0.0006053207,0.00006680471,0.0007667713],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.017655,"threshold_uncertainty_score":0.03510445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09769797011664165,"score_gpt":0.3243145633536973,"score_spread":0.2266165932370557,"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."}}