{"id":"W3182161332","doi":"10.1080/08941920.2021.1936318","title":"Salish Sea Survey: Geographic Literacy Enhancing Natural Resource Management","year":2021,"lang":"en","type":"article","venue":"Society & Natural Resources","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Karen C. Drayer Wildlife Health Center","keywords":"Outreach; Geography; Natural resource; Environmental resource management; Natural resource management; Literacy; Ecosystem management; Resource management (computing); Resource (disambiguation); Geographic information system; Ecosystem; Ecology; Political science; Biology; Cartography","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":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.003326857,0.0003377236,0.0004566231,0.0001516841,0.002512557,0.0007403176,0.0006032634,0.0002241527,0.00008683802],"category_scores_gemma":[0.0005087398,0.0003142988,0.0007189531,0.002381474,0.0005566655,0.000732309,0.0004166327,0.0006867087,0.00006906216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000170603,"about_ca_system_score_gemma":0.00006765904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002722844,"about_ca_topic_score_gemma":0.005565017,"domain_scores_codex":[0.9956393,0.000746137,0.0006985908,0.000551613,0.0013896,0.000974792],"domain_scores_gemma":[0.9976718,0.0006288976,0.0003506721,0.0004531389,0.0007109452,0.0001844825],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009289664,0.0001661935,0.2766071,0.0006150478,0.001818579,0.00007670682,0.5506204,0.00002683532,0.0002239491,0.007742933,0.1307583,0.03125109],"study_design_scores_gemma":[0.0004205179,0.0000110182,0.1675334,0.000151558,0.00004610799,0.000004825069,0.09171288,0.00004346497,0.00005785975,0.000195695,0.7393763,0.0004463625],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9167929,0.03055561,0.00004000328,0.002701889,0.002114194,0.0008040129,0.00004949552,0.0005723166,0.04636954],"genre_scores_gemma":[0.9771881,0.001448966,0.0008569081,0.00169941,0.0005087953,0.00004378626,0.0001448186,0.0000247494,0.01808448],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.608618,"threshold_uncertainty_score":0.9999309,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01050999156489275,"score_gpt":0.2791553455977782,"score_spread":0.2686453540328854,"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."}}