{"id":"W7022302759","doi":"","title":"Growing some Trees","year":2015,"lang":"en","type":"other","venue":"OpenEdition (OpenEdition)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Centre National de la Recherche Scientifique; AXA Research Fund","keywords":"Tree (set theory); Node (physics); Decision tree; Feature (linguistics); Decision tree learning","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":[0.00193802,0.001028979,0.001126834,0.002041756,0.001554023,0.002500484,0.00206454,0.001683658,0.02202956],"category_scores_gemma":[0.01993009,0.001004569,0.002599759,0.002344484,0.0008143379,0.00582159,0.002609513,0.003030441,0.01392872],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007328885,"about_ca_system_score_gemma":0.000820471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001517535,"about_ca_topic_score_gemma":0.003992331,"domain_scores_codex":[0.9984261,0.0003144961,0.0001203668,0.0004908988,0.0004757161,0.0001724505],"domain_scores_gemma":[0.9940251,0.00326781,0.0001856594,0.001278211,0.001001562,0.0002417512],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000452314,0.0001408329,0.003257127,0.001416072,0.0002264413,0.0008022669,0.001890827,0.04663784,0.006925478,0.1388773,0.2199222,0.5794514],"study_design_scores_gemma":[0.0001104218,0.0001465774,0.001379058,0.0005822171,0.0001877159,0.0008195777,0.0005599501,0.2644702,0.006213334,0.3872561,0.3382176,0.00005729149],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02123053,0.002514469,0.9240519,0.002646102,0.001027496,0.0003212797,0.008279633,0.006493315,0.03343536],"genre_scores_gemma":[0.1096483,0.001990706,0.8247976,0.001046172,0.0006807357,0.0005055287,0.01939467,0.004634427,0.0373018],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02202956,"threshold_uncertainty_score":0.0736962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02456717442886905,"score_gpt":0.2627927164072281,"score_spread":0.238225541978359,"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."}}