{"id":"W277015030","doi":"10.22004/ag.econ.168255","title":"Wetlands Retention and Optimal Management of Waterfowl Habitat under Climate Change","year":2014,"lang":"en","type":"article","venue":"AgEcon Search (University of Minnesota, USA)","topic":"Economic and Environmental Valuation","field":"Economics, Econometrics and Finance","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Wetland; Waterfowl; Climate change; Environmental science; Pothole (geology); Land use, land-use change and forestry; Habitat; Externality; Land use; Ecology; Geography; Environmental resource management; 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":[0.0005099705,0.0003283907,0.0003755485,0.0003172456,0.0004420421,0.001200964,0.0006532155,0.0009325938,0.001988623],"category_scores_gemma":[0.001819937,0.0003384674,0.0004385858,0.000347939,0.0008582163,0.001236565,0.0006864671,0.000650244,0.0001101134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002448776,"about_ca_system_score_gemma":0.002469286,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06481505,"about_ca_topic_score_gemma":0.06219566,"domain_scores_codex":[0.9998639,0.00004316816,0.000003250789,0.00002357518,0.00001019125,0.00005589551],"domain_scores_gemma":[0.9996352,0.0001564306,0.0000887282,0.000009479543,0.00003057464,0.00007947471],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003358633,0.00002660097,0.003817568,0.00001238111,0.00002277008,0.00004933534,0.00003388926,0.9824296,0.0004020346,0.01042669,0.0004493634,0.002296188],"study_design_scores_gemma":[0.00001559824,0.00003091316,0.002194077,0.000006027702,0.00001464361,0.00001510963,0.00006857266,0.9915627,0.0001145146,0.00560344,0.0003662058,0.000008122941],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.876452,0.0003133703,0.1090915,0.002259234,0.00003133633,0.00004548252,0.0004440933,0.0000761922,0.01128684],"genre_scores_gemma":[0.9939786,0.0001323445,0.003477214,0.00004236132,0.000007391606,0.00002126126,0.00004556006,0.00000819893,0.002287094],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06481505,"threshold_uncertainty_score":0.1288756,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1076861658112647,"score_gpt":0.207844758980682,"score_spread":0.1001585931694173,"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."}}