{"id":"W2894997177","doi":"10.1142/s2382624x1850025x","title":"Management of an Aquatic Invasive Weed with Uncertain Benefits and Damage Costs: The Case of <i>Elodea Canadensis</i> in Sweden","year":2018,"lang":"en","type":"article","venue":"Water Economics and Policy","topic":"Biological Control of Invasive Species","field":"Agricultural and Biological Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Vetenskapsrådet","keywords":"Weed; Elodea canadensis; Weed control; Biological dispersal; Invasive species; Population; Allee effect; Abundance (ecology); Environmental science; Ecology; Aquatic plant; Biology; Agroforestry; Natural resource economics; Macrophyte; Economics","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":[0.0007494953,0.0004428728,0.0004006917,0.0004854221,0.001155269,0.001434581,0.0005688518,0.001308519,0.000880459],"category_scores_gemma":[0.001799463,0.0002363466,0.0006259349,0.0004173239,0.0008126411,0.0008272695,0.0009906122,0.0005580648,0.00007626296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003361377,"about_ca_system_score_gemma":0.001741189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09162747,"about_ca_topic_score_gemma":0.1532275,"domain_scores_codex":[0.9995752,0.0001674658,0.00001634049,0.00003148309,0.00004201997,0.0001674652],"domain_scores_gemma":[0.9988803,0.0006462405,0.0001818778,0.00003498629,0.00008300655,0.0001736427],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001235383,0.0008643657,0.1070912,0.0003221184,0.0005301096,0.01544088,0.001583816,0.8293698,0.008468938,0.01465365,0.001900844,0.01853882],"study_design_scores_gemma":[0.0004137109,0.002128227,0.104569,0.00014782,0.0006843295,0.002943313,0.01558261,0.8449363,0.00405198,0.01640395,0.007856811,0.0002818915],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973194,0.00009540467,0.0005328263,0.0002930116,0.000003668126,0.000011234,0.0000264095,0.000005973429,0.001711958],"genre_scores_gemma":[0.9986556,0.0001279748,0.000578277,0.00002129356,0.000003047463,0.000006416039,0.00001769644,0.000002389856,0.000587322],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09162747,"threshold_uncertainty_score":0.1821883,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02273350817447263,"score_gpt":0.222527021347858,"score_spread":0.1997935131733853,"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."}}