{"id":"W7093603004","doi":"","title":"Managing Turfgrass to\\nReduce Wildlife Hazards\\nat Airports","year":2013,"lang":"","type":"article","venue":"Insecta mundi","topic":"Libraries and Information Services","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Wildlife; Vegetation (pathology); Vegetation cover; Wildlife conservation; Wildlife management; Perennial plant; Land use; Herbivore","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":[0.00040721,0.0001947635,0.00009637259,0.0007636455,0.0009904522,0.001298813,0.0004925465,0.0002548202,0.005390232],"category_scores_gemma":[0.000652458,0.00005871555,0.0001319487,0.0005178622,0.0002400835,0.0005745796,0.000534046,0.0002785114,0.001221275],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007068882,"about_ca_system_score_gemma":0.001642587,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01252564,"about_ca_topic_score_gemma":0.06529114,"domain_scores_codex":[0.9997969,0.00005393247,0.000006469393,0.00001883084,0.00005723342,0.00006666195],"domain_scores_gemma":[0.9995646,0.00006611205,0.0001037971,0.00002026876,0.00009374844,0.0001515116],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00007895606,0.0005052048,0.0495477,0.0004069253,0.00002829077,0.0005793688,0.003562994,0.0007564696,0.009755228,0.001969469,0.03491611,0.8978932],"study_design_scores_gemma":[0.00003760869,0.001041849,0.2233653,0.0006393358,0.0001006571,0.0008408937,0.02101792,0.001477447,0.006941454,0.001820706,0.742665,0.00005200036],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8208758,0.01017352,0.00595793,0.007945611,0.0002190325,0.0002800002,0.0002987008,0.000680513,0.1535689],"genre_scores_gemma":[0.9145504,0.0166944,0.01957644,0.00177964,0.0002703164,0.0001019874,0.0004857489,0.00006207998,0.04647905],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01252564,"threshold_uncertainty_score":0.0249055,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01844989025105798,"score_gpt":0.2119220528915116,"score_spread":0.1934721626404536,"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."}}