{"id":"W2988926926","doi":"10.1139/cjz-2019-0070","title":"A predictive model to diagnose pregnancy in guanacos (<i>Lama guanicoe</i>) using non-invasive methods","year":2019,"lang":"en","type":"article","venue":"Canadian Journal of Zoology","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Agencia Nacional de Promoción Científica y Tecnológica; Consejo Nacional de Investigaciones Científicas y Técnicas","keywords":"Pregnancy; Biology; Feces; Metabolite; Population; Hormone; Logistic regression; Physiology; Obstetrics; Endocrinology; Internal medicine; Medicine; Ecology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.002114096,0.001057989,0.000626415,0.001319212,0.0004777188,0.0009332987,0.0009188565,0.0006172506,0.001526463],"category_scores_gemma":[0.002696217,0.0002868661,0.0008447023,0.0004146575,0.0003304374,0.0003515448,0.0007074478,0.0008731493,0.0003095835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009047061,"about_ca_system_score_gemma":0.001201084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02565833,"about_ca_topic_score_gemma":0.01834046,"domain_scores_codex":[0.9996531,0.0001294775,0.00002294267,0.0001049762,0.00003619805,0.00005318086],"domain_scores_gemma":[0.9985978,0.0009800299,0.0001384398,0.0000338639,0.0001827787,0.00006714159],"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":[0.0007573799,0.0005343262,0.3805066,0.0001408168,0.0005636435,0.0005396874,0.0002308903,0.4866034,0.002841041,0.00176337,0.003384957,0.1221339],"study_design_scores_gemma":[0.00001867638,0.0001288962,0.01695939,0.00003119418,0.00009266344,0.00006592086,0.00007387766,0.9811032,0.0002446108,0.0007199361,0.0005464424,0.00001520727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7786032,0.001298973,0.2121951,0.001009245,0.0001643199,0.0003566566,0.001464003,0.001141775,0.003766734],"genre_scores_gemma":[0.9715422,0.0002929131,0.02474511,0.0001300356,0.0000462086,0.0002101926,0.001354794,0.00002492196,0.001653629],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02565833,"threshold_uncertainty_score":0.051018,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01923375231515252,"score_gpt":0.272210801458619,"score_spread":0.2529770491434665,"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."}}