{"id":"W2762624117","doi":"10.1038/ngeo3041","title":"Enhancing protection for vulnerable waters","year":2017,"lang":"en","type":"article","venue":"Nature Geoscience","topic":"Ecosystem dynamics and resilience","field":"Environmental Science","cited_by":221,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo; Western University","funders":"Australian Government","keywords":"Rulemaking; Scope (computer science); Government (linguistics); Environmental planning; Best practice; Freshwater ecosystem; Business; Sustainability; Uncertainty; Environmental resource management; Adaptive management; Political science; Law; Ecosystem; Ecology; Geography; Environmental science","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0007136173,0.00008858058,0.00008188605,0.00001856644,0.001894164,0.000183005,0.0006744886,0.0001261958,0.00005745774],"category_scores_gemma":[0.0003195408,0.00006628149,0.00004037173,0.00007031447,0.0001932406,0.0004510266,0.0001698193,0.0002577213,0.00006139121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008766085,"about_ca_system_score_gemma":0.00001223371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000350163,"about_ca_topic_score_gemma":0.0005761115,"domain_scores_codex":[0.9989229,0.00001531115,0.0001064133,0.0003877774,0.0002501611,0.00031744],"domain_scores_gemma":[0.9993498,0.00002076798,0.0001195086,0.0004246202,0.00001361867,0.00007172582],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00007924909,0.0001793668,0.1346392,0.0001766989,0.00001126328,0.00001778908,0.0009394939,0.003763511,0.813897,0.004366562,0.001982513,0.03994737],"study_design_scores_gemma":[0.001422311,0.0005354064,0.5032253,0.0002510195,0.00002848925,0.00008481542,0.0003265728,0.2265339,0.09676456,0.01089837,0.1584486,0.001480711],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9496301,0.00002324407,0.03924403,0.001199304,0.001437516,0.0006601327,0.00001266063,0.00004311037,0.007749918],"genre_scores_gemma":[0.9922385,0.000004374852,0.003609939,0.0001219987,0.00005424567,0.00005502911,9.759652e-7,0.00000581864,0.003909166],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7171324,"threshold_uncertainty_score":0.9994052,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006321345543477796,"score_gpt":0.2445269434365055,"score_spread":0.2382055978930277,"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."}}