{"id":"W4392874513","doi":"10.1016/j.jglr.2025.102529","title":"The aggregate economic value of Great Lakes recreational fishing trips","year":2025,"lang":"en","type":"article","venue":"Journal of Great Lakes Research","topic":"Economic and Environmental Valuation","field":"Economics, Econometrics and Finance","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Great Lakes Fishery Commission","keywords":"TRIPS architecture; Fishing; Recreation; Fishery; Recreational fishing; Value (mathematics); Aggregate (composite); Geography; Environmental science; Economics; Transport engineering; Engineering; Ecology; Mathematics; Statistics; Biology","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":[0.0005086401,0.0001498424,0.0002164753,0.0009649695,0.0002750315,0.002073045,0.0002704433,0.0004137521,0.002619978],"category_scores_gemma":[0.0050286,0.0001942639,0.0002812082,0.001673863,0.0004902364,0.001786756,0.0008044894,0.0006451845,0.0002196453],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001434868,"about_ca_system_score_gemma":0.000339811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01593542,"about_ca_topic_score_gemma":0.03097007,"domain_scores_codex":[0.9997624,0.00007584805,0.00002144501,0.00003464166,0.0000698927,0.00003588834],"domain_scores_gemma":[0.9975632,0.00119564,0.0005492141,0.0001297477,0.0002843002,0.0002779046],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007440758,0.0001475823,0.7838346,0.0001549134,0.0007151788,0.001217603,0.001128474,0.1003952,0.003163583,0.05709491,0.007705376,0.04369848],"study_design_scores_gemma":[0.00001535284,0.00007687983,0.8829344,0.00004113174,0.000123056,0.0003180142,0.002171765,0.0813951,0.0003349025,0.0279197,0.00462799,0.0000415912],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.985342,0.0004281493,0.001634083,0.0009641903,0.00001759555,0.00001040148,0.002287471,0.000015632,0.009300407],"genre_scores_gemma":[0.9973394,0.0001998972,0.000160194,0.00001935428,0.00002792308,0.000004758275,0.0005098467,0.000003030792,0.001735477],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01593542,"threshold_uncertainty_score":0.03168535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1290311373743306,"score_gpt":0.3128449831657489,"score_spread":0.1838138457914183,"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."}}