{"id":"W3012664102","doi":"","title":"The importance of geospatial inputs in assessing fine-scale landscape genetic patterns of a temperate treefrog","year":2018,"lang":"en","type":"article","venue":"QSpace (Queen's University Library)","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Geospatial analysis; Temperate climate; Scale (ratio); Geography; Physical geography; Ecology; Environmental science; Environmental resource management; Biology; Cartography","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00004453944,0.000102528,0.0001407842,0.00004173759,0.0001156382,0.00002516173,0.0003080636,0.00005902104,0.008370656],"category_scores_gemma":[0.000008738854,0.00008876045,0.00005239498,0.0003503003,0.0002357084,0.0004292369,0.000277784,0.00007975705,0.00003898348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007694875,"about_ca_system_score_gemma":0.00002387978,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002240855,"about_ca_topic_score_gemma":0.00491412,"domain_scores_codex":[0.9991995,0.00006555241,0.0001432755,0.0001974291,0.000180374,0.0002138501],"domain_scores_gemma":[0.9995037,0.00003845723,0.0001432527,0.0002400387,0.00001095264,0.0000636273],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003549122,0.00004956608,0.9907843,0.000007915619,0.000004917428,0.000009015517,0.0002725243,0.000009624992,0.0002559733,0.0000669236,0.008300497,0.0002032567],"study_design_scores_gemma":[0.0003770682,0.00007344166,0.9800745,0.00002084406,0.000007805859,2.133583e-7,0.001691874,0.0000380814,0.007350788,0.00001865346,0.01024232,0.0001044559],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.986681,0.000006172949,0.00007320472,0.005417933,0.00005107101,0.0001005267,0.00006968052,0.00002290005,0.007577441],"genre_scores_gemma":[0.9964102,0.00008147799,0.0001572527,0.0000649649,0.00001848934,4.482499e-7,0.00002178269,0.000007872984,0.00323754],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01070984,"threshold_uncertainty_score":0.9925358,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007805305747948168,"score_gpt":0.1951969084730199,"score_spread":0.1873916027250718,"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."}}