{"id":"W2137864274","doi":"10.1111/conl.12126","title":"Maximizing Return on Investment for Island Restoration and Species Conservation","year":2014,"lang":"en","type":"article","venue":"Conservation Letters","topic":"Animal Ecology and Behavior Studies","field":"Environmental Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Environmental resource management; Investment (military); Return on investment; Population; Resource (disambiguation); Business; Scale (ratio); Psychological intervention; Environmental planning; Natural resource economics; Cost–benefit analysis; Biodiversity; Ecology; Geography; Economics; Computer science; Biology; Production (economics); Political science","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":[0.003301065,0.0006608379,0.0007813825,0.001379606,0.0004003005,0.00231559,0.00083925,0.001114926,0.005291291],"category_scores_gemma":[0.009882132,0.0002826354,0.0004868066,0.001043389,0.0009655411,0.001461956,0.001428879,0.0009078261,0.0004046629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003615457,"about_ca_system_score_gemma":0.004203972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008819292,"about_ca_topic_score_gemma":0.01858321,"domain_scores_codex":[0.9973211,0.001451044,0.00006753663,0.0002327592,0.0003696983,0.0005579233],"domain_scores_gemma":[0.9968534,0.00169215,0.0006040264,0.0001848778,0.0003586189,0.0003069651],"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":[0.0005141201,0.0003398857,0.03940119,0.0006076635,0.0004190519,0.0002702472,0.0002954556,0.705507,0.005775725,0.08970485,0.007093352,0.1500714],"study_design_scores_gemma":[0.0001211716,0.000971019,0.08062246,0.0006235412,0.0005405772,0.0005202513,0.002267291,0.73414,0.005659023,0.1517591,0.02266116,0.0001144232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5698557,0.002745286,0.3173108,0.00611801,0.0001231622,0.0008535986,0.001068475,0.0004534844,0.1014715],"genre_scores_gemma":[0.9707656,0.0003014011,0.02679586,0.0001218197,0.00001154947,0.0001124603,0.00008455433,0.00002471773,0.001781936],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008819292,"threshold_uncertainty_score":0.02623206,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02984857859558751,"score_gpt":0.2327835148515796,"score_spread":0.2029349362559921,"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."}}