{"id":"W2145444191","doi":"10.1111/mec.12931","title":"Testing a ‘genes‐to‐ecosystems’ approach to understanding aquatic–terrestrial linkages","year":2014,"lang":"en","type":"article","venue":"Molecular Ecology","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation; Genome Canada","keywords":"Biology; Ecosystem; Terrestrial ecosystem; Aquatic ecosystem; Ecology; Gene; Computational biology; Evolutionary biology; Genetics","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":[0.006744731,0.002134626,0.0008582,0.002123412,0.0007987067,0.001456525,0.001628522,0.001458292,0.003466583],"category_scores_gemma":[0.01084541,0.0005212,0.001309021,0.001498515,0.002934134,0.003157608,0.002526792,0.001905886,0.000210905],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001182216,"about_ca_system_score_gemma":0.00161129,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003740886,"about_ca_topic_score_gemma":0.0035871,"domain_scores_codex":[0.9957607,0.002395058,0.0001340496,0.001268329,0.0002697218,0.000172158],"domain_scores_gemma":[0.9845391,0.01216886,0.001415352,0.001134401,0.0003349795,0.0004073129],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002081659,0.001849286,0.663587,0.001602468,0.006213953,0.002203199,0.003567827,0.05367567,0.1023059,0.06515804,0.0008986687,0.09685632],"study_design_scores_gemma":[0.0003374586,0.003313028,0.6674864,0.0002271936,0.001679592,0.0005122793,0.003038628,0.08906048,0.01456587,0.2127315,0.006816452,0.0002311626],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9029192,0.0006852956,0.08685027,0.002087499,0.00005552324,0.000207802,0.001810722,0.0001691121,0.005214591],"genre_scores_gemma":[0.9468282,0.0003842088,0.04998175,0.001387709,0.00003244102,0.0003528795,0.0005330751,0.00004644015,0.0004533504],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006744731,"threshold_uncertainty_score":0.03566998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0390066616842908,"score_gpt":0.2466346007836348,"score_spread":0.207627939099344,"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."}}