{"id":"W4411463308","doi":"10.1002/aps3.70015","title":"Applying interpretable machine learning to assess intraspecific trait divergence under landscape‐scale population differentiation","year":2025,"lang":"en","type":"article","venue":"Applications in Plant Sciences","topic":"Morphological variations and asymmetry","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"National Institute on Aging; University of Central Florida","keywords":"Biology; Intraspecific competition; Trait; Divergence (linguistics); Evolutionary biology; Scale (ratio); Population; Ecology; Demography; Cartography; Computer science; Linguistics","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":[0.003128885,0.0004285748,0.0003365764,0.001494553,0.0002904457,0.0008364334,0.0005119345,0.0005103941,0.0009732154],"category_scores_gemma":[0.006620904,0.0001133774,0.0004450209,0.0007202249,0.0004492891,0.0004576262,0.0005774252,0.0005295522,0.0001777317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003970478,"about_ca_system_score_gemma":0.0002690043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001422298,"about_ca_topic_score_gemma":0.002195827,"domain_scores_codex":[0.9992554,0.0003759465,0.00004748546,0.0001983246,0.00007935553,0.00004344068],"domain_scores_gemma":[0.9971603,0.001839797,0.0004030334,0.0003296777,0.0002069755,0.00006022561],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007228898,0.0004869379,0.6405458,0.0001770426,0.0009479268,0.0003123244,0.0006881689,0.1030817,0.04275783,0.003283662,0.001153204,0.2058426],"study_design_scores_gemma":[0.00005245818,0.0002793302,0.2932329,0.00002189186,0.00009793928,0.0001510479,0.0002968136,0.6918963,0.004877419,0.008366098,0.000699185,0.00002863234],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9162825,0.00007503833,0.08177652,0.0001049246,0.000009532204,0.00004088936,0.0004783716,0.0002792576,0.0009530618],"genre_scores_gemma":[0.9835735,0.000009579941,0.01581938,0.0000279621,0.000007564263,0.00002707007,0.0003982663,0.0000173469,0.0001192312],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003128885,"threshold_uncertainty_score":0.01654732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06143081529557567,"score_gpt":0.3191278510825231,"score_spread":0.2576970357869475,"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."}}