{"id":"W6961028630","doi":"10.1371/journal.pone.0136427.t005","title":"Brain regions showing correlations between the strength of the resting-state networks in the seed-based analysis and the scores on the posttraumatic growth inventory.","year":2015,"lang":"en","type":"dataset","venue":"Figshare","topic":"Seed and Plant Biochemistry","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Statistical analysis; Correlation; Neuroimaging; Intensity (physics)","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":[0.0006026545,0.001308815,0.001267272,0.001519168,0.0006198162,0.001324266,0.002617716,0.001510941,0.06584547],"category_scores_gemma":[0.004238577,0.0004433114,0.001556598,0.001760583,0.0002743597,0.0006751499,0.00145527,0.001061974,0.0242605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007889463,"about_ca_system_score_gemma":0.001367568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01719313,"about_ca_topic_score_gemma":0.06179166,"domain_scores_codex":[0.9997522,0.00002593668,0.00002471715,0.000119436,0.00003300698,0.0000446861],"domain_scores_gemma":[0.9990995,0.0004197909,0.0001147627,0.0001464367,0.0001383732,0.00008103957],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0004715183,0.00005324412,0.008856347,0.00344881,0.000681881,0.0001853157,0.00009101993,0.0008465704,0.0008708094,0.001079618,0.9755102,0.007904548],"study_design_scores_gemma":[0.003133662,0.0001334298,0.166035,0.002074034,0.002254299,0.001339062,0.0003244137,0.002596893,0.002323696,0.009158742,0.8104684,0.0001583444],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001274817,0.0003024733,0.0001897925,0.00009325059,0.00003144251,0.00001899606,0.9970993,0.0003022893,0.0006876651],"genre_scores_gemma":[0.009432388,0.0002265111,0.001014216,0.0001060806,0.00002029237,0.0002061134,0.9874807,0.0001654965,0.001348168],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06584547,"threshold_uncertainty_score":0.220275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06606145970904227,"score_gpt":0.2413887464471541,"score_spread":0.1753272867381118,"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."}}