{"id":"W6923247022","doi":"10.1371/journal.pone.0172194.g001","title":"Mixing diagrams of grizzly bear hair.","year":2017,"lang":"en","type":"other","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); Hydrology (agriculture); Limiting; Diagram","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":[0.0005927088,0.0002560763,0.0002285387,0.001913759,0.0005297028,0.0008623772,0.000478679,0.0006515352,0.06707078],"category_scores_gemma":[0.002849135,0.0002657239,0.0005497152,0.0008247262,0.0002646236,0.0008809373,0.0005017527,0.0004589263,0.005520653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007688873,"about_ca_system_score_gemma":0.00041124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01506459,"about_ca_topic_score_gemma":0.01615956,"domain_scores_codex":[0.9998198,0.00003753345,0.00000569331,0.00005841481,0.00004843792,0.00003014362],"domain_scores_gemma":[0.9989856,0.0003740456,0.0001152207,0.0001055525,0.0002829121,0.0001365563],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001635803,0.0001968496,0.08236077,0.0006026931,0.0003729942,0.0008847312,0.00203619,0.2009289,0.04557283,0.2826644,0.1205427,0.2622011],"study_design_scores_gemma":[0.0001057842,0.0002028754,0.196141,0.0002582213,0.0001275258,0.0007838916,0.001034386,0.5115741,0.009978269,0.1213334,0.1582566,0.0002038942],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.443464,0.001871392,0.2948928,0.00115413,0.000469654,0.0002711964,0.02994797,0.005438454,0.2224903],"genre_scores_gemma":[0.9199534,0.0003850388,0.03873715,0.0001026436,0.00005030012,0.00009233301,0.00925097,0.0008771774,0.03055103],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.06707078,"threshold_uncertainty_score":0.2243741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08087136742365568,"score_gpt":0.2262503480456055,"score_spread":0.1453789806219498,"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."}}