{"id":"W7072029996","doi":"","title":"Using dietary analyses to reduce the\\nrisk of wildlife–aircraft collisions","year":2011,"lang":"en","type":"article","venue":"Insecta mundi","topic":"Wildlife-Road Interactions and Conservation","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Wildlife Research Center; Animal and Plant Health Inspection Service; U.S. Air Force; U.S. Department of Agriculture","keywords":"Wildlife; Civil aviation; Aviation; Habitat; Wildlife management; Aviation safety","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":[0.003426677,0.001234117,0.0008726382,0.003123365,0.0009253272,0.001519181,0.0007104232,0.000838497,0.004557173],"category_scores_gemma":[0.007624514,0.0004979126,0.0008186326,0.001716987,0.0004757963,0.0008659538,0.0009958161,0.0008389617,0.001513334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006714851,"about_ca_system_score_gemma":0.0009154589,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009778953,"about_ca_topic_score_gemma":0.03768224,"domain_scores_codex":[0.9978515,0.0006378855,0.0002964569,0.0004769193,0.0006277275,0.0001095836],"domain_scores_gemma":[0.9958282,0.000999935,0.001180466,0.0004794047,0.001405821,0.0001061536],"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":[0.002862861,0.005267029,0.4182652,0.007121107,0.002577718,0.0004536639,0.003567441,0.001073747,0.07386774,0.001197334,0.01844783,0.4652985],"study_design_scores_gemma":[0.0001353176,0.004585973,0.9181413,0.0008463598,0.001176629,0.0002964098,0.001474564,0.002416479,0.01956002,0.001311396,0.04993657,0.0001190139],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8617873,0.009813018,0.074936,0.002087039,0.0009230961,0.003473087,0.0127601,0.001306331,0.03291412],"genre_scores_gemma":[0.744819,0.007648765,0.2118431,0.003326721,0.0002121881,0.002775509,0.007607379,0.0003741588,0.02139307],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009778953,"threshold_uncertainty_score":0.01944405,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1913880444470771,"score_gpt":0.3530982206642047,"score_spread":0.1617101762171275,"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."}}