{"id":"W4406392906","doi":"10.1016/j.jglr.2025.102512","title":"Resourcing Michigan’s coastal decision-makers: Assessing needs &amp; opportunities","year":2025,"lang":"en","type":"article","venue":"Journal of Great Lakes Research","topic":"Coastal and Marine Management","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Michigan Sea Grant, University of Michigan","keywords":"Business; Environmental planning; Environmental resource management; Oceanography; Geography; Environmental science; Geology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01048173,0.0003507817,0.0001937227,0.001221656,0.005493832,0.009264939,0.001609915,0.00242397,0.009022125],"category_scores_gemma":[0.02470342,0.0002622893,0.0002145154,0.001628868,0.001289971,0.004029511,0.004275957,0.001148224,0.000924763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007002084,"about_ca_system_score_gemma":0.03013488,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06785903,"about_ca_topic_score_gemma":0.2324467,"domain_scores_codex":[0.9940705,0.003517707,0.0001641045,0.0002272695,0.0007582105,0.00126226],"domain_scores_gemma":[0.9795946,0.007117478,0.001564285,0.0004756822,0.003528496,0.007719291],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005666412,0.001553098,0.4611343,0.0005780955,0.000225889,0.001968713,0.04748592,0.003543791,0.001803093,0.03364822,0.09993609,0.3475561],"study_design_scores_gemma":[0.0001558051,0.0008147822,0.2473052,0.002093754,0.0001686262,0.0005430677,0.421224,0.01921834,0.002532755,0.02700778,0.2787341,0.0002017947],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7944635,0.0009169349,0.001106823,0.09620073,0.0001305929,0.0002406668,0.0006746363,0.00005100178,0.1062151],"genre_scores_gemma":[0.9884433,0.000581247,0.003315768,0.002523917,0.00005955361,0.000186274,0.0001941533,0.00001224649,0.004683589],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06785903,"threshold_uncertainty_score":0.1349281,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08593643035299096,"score_gpt":0.359761290107583,"score_spread":0.273824859754592,"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."}}