{"id":"W2131662444","doi":"10.1111/jpn.12391","title":"Using heart rate to predict energy expenditure in large domestic dogs","year":2015,"lang":"en","type":"article","venue":"Journal of Animal Physiology and Animal Nutrition","topic":"Cardiovascular Conditions and Treatments","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Energy expenditure; Heart rate; Regression analysis; Treadmill; Linear regression; Animal science; Regression; Mathematics; Metabolic rate; Statistics; Medicine; Physical therapy; Internal medicine; Blood pressure; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002105119,0.0001100918,0.0003931217,0.0002116819,0.00004635464,0.00001054734,0.00002868649,0.00009503048,0.00001668707],"category_scores_gemma":[0.00003969272,0.00008958917,0.0001452411,0.000136053,0.00003432702,0.0001589398,0.00002494394,0.000153879,0.000003232964],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000685182,"about_ca_system_score_gemma":0.00006226777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001865721,"about_ca_topic_score_gemma":0.000004325809,"domain_scores_codex":[0.9992152,0.00009555741,0.0002521484,0.0001396239,0.0001218057,0.0001756976],"domain_scores_gemma":[0.9995025,0.00002483522,0.00008151915,0.00006813098,0.0001320163,0.0001909954],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.009462415,0.00147471,0.0115632,0.00008647366,0.0002729835,0.001871388,0.0001422699,0.0000265977,0.9728477,0.0005249537,0.001640346,0.00008697891],"study_design_scores_gemma":[0.01373072,0.01091235,0.9546587,0.0005349245,0.0003134487,0.009677414,0.0003668461,0.0003029475,0.004498982,0.001868088,0.002941255,0.0001942884],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9978995,0.001386413,0.0001055918,0.000356027,0.00007978721,0.0001051261,0.00001552696,0.000006383423,0.00004568052],"genre_scores_gemma":[0.9982491,0.0001313115,0.0008001944,0.0003175914,0.0004646412,0.000004868394,0.00001716893,0.000009438405,0.000005693959],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9683487,"threshold_uncertainty_score":0.365334,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02753423786843452,"score_gpt":0.3068797455388388,"score_spread":0.2793455076704043,"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."}}