{"id":"W2026901007","doi":"10.1371/journal.pone.0069885","title":"GenGIS 2: Geospatial Analysis of Traditional and Genetic Biodiversity, with New Gradient Algorithms and an Extensible Plugin Framework","year":2013,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":118,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick; Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Genome Atlantic; Killam Trusts; Canada Research Chairs; Government of Canada; Canadian Institutes of Health Research; Genome Canada; Ontario Genomics; Ontario Genomics Institute","keywords":"Plug-in; Geospatial analysis; Computer science; Data mining; Geographic information system; Source code; Geography; Cartography; Programming language","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00002617771,0.0001113876,0.0002096263,0.00007196728,0.00006459127,0.00001921802,0.00006735323,0.00007050258,0.00003549075],"category_scores_gemma":[0.00001088594,0.0001006024,0.00003307405,0.0001110378,0.0001109021,0.000001606187,0.00005500796,0.00004095544,0.000001163261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003735058,"about_ca_system_score_gemma":0.00002201255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003850645,"about_ca_topic_score_gemma":0.0001278603,"domain_scores_codex":[0.9993487,0.00001513989,0.0001101482,0.0002727341,0.0001196511,0.0001336202],"domain_scores_gemma":[0.9995511,0.00001205439,0.00005434447,0.000174077,0.00008652825,0.0001219032],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00007660416,0.0007668856,0.4298492,0.00004736011,0.006286011,0.000003577714,0.0006651956,0.0002716997,0.557208,0.000081282,0.0002303812,0.004513767],"study_design_scores_gemma":[0.0004130831,0.001019137,0.9685192,0.00001389235,0.001264829,0.000003379733,0.00009805511,0.000977826,0.02677262,0.0006801029,0.00002728498,0.0002105704],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973904,0.001323024,0.0008874511,0.0001269411,0.0000107092,0.0001470662,0.00009024359,0.000002219811,0.00002189123],"genre_scores_gemma":[0.9639124,0.0007919247,0.03502131,0.00009089328,0.00008732147,0.000007789599,0.00005801437,0.000006097552,0.0000242344],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.53867,"threshold_uncertainty_score":0.4102445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03087703834212822,"score_gpt":0.2049351275782817,"score_spread":0.1740580892361535,"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."}}