{"id":"W1968244755","doi":"10.1080/03632415.2013.848344","title":"Use of Geographic Information Systems by Fisheries Management Agencies","year":2013,"lang":"en","type":"article","venue":"Fisheries","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Kansas Department of Wildlife and Parks; Northwestern Division, U.S. Army Corps of Engineers","keywords":"Fisheries management; Agency (philosophy); Work (physics); Geographic information system; Business; Environmental resource management; Fisheries science; Fishery; Service (business); Fish <Actinopterygii>; Fisheries law; Fisheries Research; Environmental planning; Geography; Fishing; Marketing; Engineering; Cartography; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007101053,0.0001940277,0.0002071119,0.003837225,0.001274766,0.002519758,0.000834747,0.0002631131,0.003564114],"category_scores_gemma":[0.02863103,0.0002802566,0.000326209,0.006084021,0.0008469389,0.001416128,0.001888908,0.0004386471,0.0005152262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003292543,"about_ca_system_score_gemma":0.004997362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09318896,"about_ca_topic_score_gemma":0.09495517,"domain_scores_codex":[0.9861457,0.006855065,0.001723656,0.000967045,0.003524244,0.0007842965],"domain_scores_gemma":[0.9432765,0.0208045,0.01397135,0.002805088,0.01587148,0.003271009],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001007716,0.00009660999,0.8804977,0.0002611813,0.00006112793,0.0001514778,0.007509413,0.0004784243,0.0009868419,0.0007227209,0.004771266,0.1043625],"study_design_scores_gemma":[0.00002245724,0.0002074675,0.916387,0.0005135782,0.00005335418,0.0002950191,0.0349874,0.00189751,0.0009574676,0.0002729443,0.04434623,0.00005943925],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9734015,0.001383404,0.001160776,0.001753518,0.00003393526,0.0001562653,0.001702465,0.00008570009,0.02032243],"genre_scores_gemma":[0.9945522,0.001099043,0.001773367,0.0001526133,0.0000158607,0.00008068596,0.0006139022,0.00001423434,0.001697977],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09318896,"threshold_uncertainty_score":0.1852931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02306805697141956,"score_gpt":0.1855176573953376,"score_spread":0.1624496004239181,"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."}}