{"id":"W2073633025","doi":"10.1016/j.jenvman.2005.04.029","title":"Multi-stage sampling for large scale natural resources surveys: A case study of rice and waterfowl","year":2005,"lang":"en","type":"article","venue":"Journal of Environmental Management","topic":"Rangeland and Wildlife Management","field":"Environmental Science","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Institute for Wetland and Waterfowl Research, Ducks Unlimited Canada","keywords":"Sampling (signal processing); Waterfowl; Sampling design; Stratified sampling; Environmental science; Abundance (ecology); Scale (ratio); Habitat; Sample (material); Statistics; Paddy field; Hydrology (agriculture); Geography; Ecology; Mathematics; Biology; Cartography; Population; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.006422221,0.0005077154,0.0003863471,0.0007547414,0.002785328,0.000590986,0.001436167,0.001350445,0.001489146],"category_scores_gemma":[0.007561659,0.0004058165,0.0005751898,0.0008668044,0.0008534574,0.0008866067,0.0008447643,0.001010026,0.0002758563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001334418,"about_ca_system_score_gemma":0.002497905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06818931,"about_ca_topic_score_gemma":0.3038549,"domain_scores_codex":[0.9968593,0.002072531,0.00008602334,0.0002662106,0.0002671211,0.0004487156],"domain_scores_gemma":[0.9935818,0.003874993,0.0006848465,0.000596191,0.0006006825,0.0006616535],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001171872,0.003595512,0.7576105,0.0003668657,0.0002025138,0.0110972,0.02735359,0.004187583,0.01993772,0.001055548,0.002402615,0.1710186],"study_design_scores_gemma":[0.000339937,0.006217163,0.8799534,0.0001936786,0.0003315441,0.01036676,0.05294132,0.03057633,0.00859786,0.002389408,0.007928932,0.0001636955],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9883083,0.00008986332,0.009638854,0.0002079297,0.000008979594,0.0005317023,0.00006434602,0.00002235536,0.00112779],"genre_scores_gemma":[0.9634826,0.0001099988,0.03442755,0.000175892,0.000009689589,0.0004981285,0.0000750315,0.00001305432,0.001208116],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06818931,"threshold_uncertainty_score":0.1355848,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02297546093581547,"score_gpt":0.2646764320697934,"score_spread":0.241700971133978,"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."}}