{"id":"W2995883207","doi":"10.14264/uql.2019.896","title":"Improving wildlife detection dog team selection and training","year":2019,"lang":"en","type":"dissertation","venue":"The University of Queensland","topic":"Human-Animal Interaction Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Wildlife; Selection (genetic algorithm); Breed; Animal welfare; Wildlife management; Geography; Environmental resource management; Computer science; Biology; Ecology; Artificial intelligence; Environmental 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":[0.001793401,0.000283258,0.0003080069,0.0005145168,0.0004690306,0.0006359784,0.000934593,0.0004203107,0.003595269],"category_scores_gemma":[0.003317926,0.0001494671,0.0002719282,0.0002251008,0.0002842666,0.0005792497,0.0008976674,0.0003954168,0.001195693],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004927414,"about_ca_system_score_gemma":0.0006995772,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00192424,"about_ca_topic_score_gemma":0.004849999,"domain_scores_codex":[0.998801,0.0003865428,0.00006537677,0.0002239048,0.0003049241,0.0002182303],"domain_scores_gemma":[0.9982933,0.0003215,0.0004644718,0.0001139728,0.0003590311,0.0004477001],"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":[0.0003521631,0.005046833,0.26418,0.0007893272,0.0000729286,0.0003348434,0.005129377,0.001974857,0.01767816,0.0005100562,0.0108248,0.6931066],"study_design_scores_gemma":[0.0001017761,0.007195313,0.938262,0.000621815,0.00008425553,0.0004924338,0.006516538,0.003896398,0.006287675,0.0004846019,0.03601036,0.00004659409],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9860343,0.0004898742,0.005210284,0.0008854475,0.0000718569,0.000244682,0.00006454199,0.0001110694,0.006887972],"genre_scores_gemma":[0.9591076,0.0008973878,0.02939796,0.0002638375,0.00007104448,0.0004833073,0.0002768386,0.00002697401,0.009475105],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003595269,"threshold_uncertainty_score":0.01202732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0108647217728525,"score_gpt":0.2607905819435528,"score_spread":0.2499258601707003,"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."}}