{"id":"W6942220457","doi":"10.1371/journal.pone.0233257.t004","title":"Results of the logistic regression analysis for the entire cohort, Golden Retriever, and Labrador Retriever.","year":2020,"lang":"en","type":"dataset","venue":"Figshare","topic":"Mycorrhizal Fungi and Plant Interactions","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Logistic regression; Cross-sectional regression; Regression analysis; Regression; Factor regression model; Multinomial logistic regression","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":[0.00282448,0.001474886,0.001639204,0.00189595,0.0005299523,0.001502101,0.002577021,0.001656225,0.0552769],"category_scores_gemma":[0.01577622,0.0005353752,0.002170596,0.002525178,0.0002585837,0.001293985,0.001339638,0.00161574,0.02991604],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001096435,"about_ca_system_score_gemma":0.001871395,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03071325,"about_ca_topic_score_gemma":0.06491379,"domain_scores_codex":[0.9986221,0.0002054616,0.00018455,0.0005581827,0.0002628122,0.0001669709],"domain_scores_gemma":[0.9946595,0.002437067,0.0006632238,0.0007624336,0.001166533,0.0003113346],"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":[0.0003294816,0.00003369302,0.01443503,0.001048405,0.000503992,0.00005197968,0.00002104447,0.0004215121,0.0001101172,0.000293757,0.978305,0.004446087],"study_design_scores_gemma":[0.002453753,0.0001559429,0.1665838,0.002286332,0.002345752,0.000575058,0.0003480613,0.002399697,0.0006168766,0.003112389,0.8189496,0.0001726591],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0009828899,0.0002598772,0.0001706076,0.0001532575,0.00006170053,0.00001682745,0.9973792,0.0001364033,0.000839176],"genre_scores_gemma":[0.006407889,0.0001944852,0.0006690125,0.0002598753,0.00004892957,0.0002520503,0.9890619,0.0002131906,0.002892693],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0552769,"threshold_uncertainty_score":0.1849197,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04583137110274751,"score_gpt":0.2560303219031809,"score_spread":0.2101989508004334,"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."}}