{"id":"W4296793486","doi":"10.1371/journal.pone.0274168","title":"Learning how a tree branches out: A statistical modeling approach","year":2022,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Forest ecology and management","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Branching (polymer chemistry); Computer science; Tree structure; Hierarchy; Tree (set theory); Statistical model; Artificial intelligence; Data structure; Trunk; Algorithm; Pattern recognition (psychology); Mathematics; Statistics; Biology; Combinatorics; Binary tree; Ecology","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":[0.01196065,0.00150877,0.001919012,0.005866501,0.001010808,0.004428256,0.003916888,0.002593723,0.002091699],"category_scores_gemma":[0.02497858,0.0008464654,0.002687376,0.004275238,0.002889717,0.004265335,0.002207293,0.00331082,0.000742232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002367776,"about_ca_system_score_gemma":0.001918737,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00937746,"about_ca_topic_score_gemma":0.005962612,"domain_scores_codex":[0.9954123,0.002448041,0.0002412595,0.001027707,0.0006528206,0.0002179476],"domain_scores_gemma":[0.971749,0.02443845,0.001593009,0.0008494991,0.001047708,0.0003223219],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008493516,0.0001687489,0.01763742,0.0004580923,0.0006190611,0.0003488867,0.001038944,0.6559728,0.001174647,0.2223177,0.003882131,0.09629667],"study_design_scores_gemma":[0.000006611253,0.00005354694,0.00108445,0.00007019173,0.0000525813,0.00007414282,0.0001249399,0.8764451,0.0001756167,0.1197134,0.002162392,0.00003696672],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00991526,0.00108127,0.9860803,0.001112022,0.00005046997,0.00006223306,0.000334692,0.0002999319,0.001063852],"genre_scores_gemma":[0.463221,0.005088068,0.523972,0.0007720655,0.0007265072,0.0009319787,0.001815453,0.000300538,0.003172388],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01196065,"threshold_uncertainty_score":0.06325477,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04197424740926196,"score_gpt":0.199459897133438,"score_spread":0.1574856497241761,"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."}}