{"id":"W4391480729","doi":"10.3389/fevo.2024.1304748","title":"Dakota skipper distribution model for North Dakota, South Dakota, and Minnesota aids conservation planning under changing climate scenarios","year":2024,"lang":"en","type":"article","venue":"Frontiers in Ecology and Evolution","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Geography; Distribution (mathematics); Climate change; Ecology; Environmental resource management; Environmental science; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003925761,0.0001754062,0.0001966954,0.0001139636,0.0004089243,0.00005707126,0.00007550684,0.0001935046,0.0002196329],"category_scores_gemma":[0.00004254872,0.0001801359,0.00004076174,0.0002951353,0.0002579147,0.000402672,0.0001387752,0.0001542194,0.00003910545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006321554,"about_ca_system_score_gemma":0.00001910765,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002745248,"about_ca_topic_score_gemma":0.0004018217,"domain_scores_codex":[0.9986045,0.00004291271,0.0002433332,0.0004514651,0.0001080161,0.0005498217],"domain_scores_gemma":[0.9996862,0.00004419757,0.00006346039,0.0001128876,0.00001391403,0.00007928544],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008375975,0.00003417058,0.9789473,0.00006360187,0.00001756266,0.000003218638,0.0008262783,0.004174728,0.0000829874,0.002193206,0.01309516,0.0004780313],"study_design_scores_gemma":[0.0003889161,0.00005063972,0.5777159,0.00002453177,0.00003276108,0.000007901816,0.002151406,0.4171914,0.000005064619,0.0007505997,0.001530046,0.0001507897],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9291588,0.0006321617,0.06779568,0.0005740384,0.0006579096,0.0003590553,0.0005092123,0.0000660348,0.0002471194],"genre_scores_gemma":[0.9975972,0.0001849625,0.0006001497,0.0002223588,0.00003953421,0.00009333609,0.0009853987,0.00001421545,0.0002628116],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4130166,"threshold_uncertainty_score":0.7345727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01666860639649945,"score_gpt":0.232976351553902,"score_spread":0.2163077451574026,"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."}}