{"id":"W2901807390","doi":"10.1016/j.fishres.2018.11.007","title":"Predicting spatial patterns of recreational boating to understand potential impacts to fisheries and aquatic ecosystems","year":2018,"lang":"en","type":"article","venue":"Fisheries Research","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hatch (Canada); Fisheries and Oceans Canada; Ministry of Natural Resources and Forestry","funders":"","keywords":"Fishing; Recreation; Recreational fishing; Fishery; Geography; Ecosystem; Tourism; Biomass (ecology); Commercial fishing; Environmental science; Ecology; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009058013,0.0001088089,0.0001673097,0.00009388151,0.0005687475,0.00007420385,0.0001883444,0.00005504869,0.001955909],"category_scores_gemma":[0.0007210223,0.0001026575,0.0000191321,0.000251049,0.0003616016,0.0002286021,0.0008043465,0.000120421,0.00008984261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001368225,"about_ca_system_score_gemma":0.00001685491,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004663197,"about_ca_topic_score_gemma":0.05351847,"domain_scores_codex":[0.9983026,0.0001653828,0.0002376449,0.0003228137,0.000535922,0.0004356276],"domain_scores_gemma":[0.9994045,0.000192182,0.00005084017,0.000175003,0.00003640809,0.0001410715],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001191887,0.00002745159,0.9661409,0.00005320572,0.00003172906,0.000004135513,0.005599165,0.00001035282,0.0005161067,0.00003248439,0.02638208,0.001083196],"study_design_scores_gemma":[0.0002234185,0.0009475817,0.9876344,0.00006734845,0.000007977717,0.000002261478,0.005557513,0.001007162,0.0007219748,0.0002850058,0.003424767,0.0001206477],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.978469,0.00000484497,0.002181712,0.003114986,0.000141745,0.0005220452,0.00001819767,0.00002205023,0.01552546],"genre_scores_gemma":[0.9975956,0.00001568837,0.00059324,0.0001777554,0.0001347115,0.00004048487,0.00000663016,0.00001238377,0.001423507],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04885527,"threshold_uncertainty_score":0.9989564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04189728845186256,"score_gpt":0.2997809751020925,"score_spread":0.2578836866502299,"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."}}