{"id":"W6923117956","doi":"10.1371/journal.pone.0097968.s001","title":"Supporting Information S1 - Macronutrient Optimization and Seasonal Diet Mixing in a Large Omnivore, the Grizzly Bear: A Geometric Analysis","year":2015,"lang":"en","type":"dataset","venue":"Figshare","topic":"Diverse Scientific and Economic Studies","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mixing (physics); Seasonality; Grizzly Bears; Geometric mean","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008291008,0.001583764,0.001004029,0.001630571,0.00114748,0.00215696,0.002633858,0.001303002,0.2133758],"category_scores_gemma":[0.005459774,0.0006804169,0.00136138,0.003556787,0.0004097169,0.0008461633,0.001148264,0.001265218,0.06260785],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003493949,"about_ca_system_score_gemma":0.005457566,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.6498618,"about_ca_topic_score_gemma":0.7929326,"domain_scores_codex":[0.9997353,0.00002716068,0.000017587,0.00006840983,0.00007600428,0.00007540181],"domain_scores_gemma":[0.9977239,0.0005781826,0.0001458204,0.0002262187,0.00110104,0.0002248666],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006029363,0.00002563704,0.006678404,0.0003443965,0.00004785648,0.00001826735,0.00003741832,0.001081947,0.00007341447,0.0004208598,0.9889849,0.002226669],"study_design_scores_gemma":[0.001024488,0.00004381508,0.09736161,0.001406139,0.00018955,0.0001606673,0.000663669,0.005629825,0.0003997035,0.004328904,0.888672,0.0001194902],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002515569,0.00002699327,0.00004759126,0.00004098904,0.00001104558,0.000007319546,0.9992225,0.0000530521,0.0003388768],"genre_scores_gemma":[0.003424359,0.00007892812,0.0006626045,0.00007858174,0.00001293262,0.00007181596,0.9937208,0.00008888289,0.001861257],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.7866242,"threshold_uncertainty_score":0.7138131,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03244858231938361,"score_gpt":0.2354719601201335,"score_spread":0.2030233778007499,"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."}}