{"id":"W4393677694","doi":"10.5281/zenodo.10031957","title":"Parcellating the parcellation issue - a proof of concept for reproducible analyses using Neurolibre","year":2023,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Data Analysis with R","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Proof of concept; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01854162,0.00389868,0.002777292,0.00437368,0.003054793,0.006743945,0.005264457,0.003212029,0.06124516],"category_scores_gemma":[0.07102026,0.001433297,0.004577424,0.004178727,0.002077187,0.002811451,0.005397551,0.004401789,0.1014289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001802121,"about_ca_system_score_gemma":0.006659689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006778936,"about_ca_topic_score_gemma":0.01494297,"domain_scores_codex":[0.986527,0.003558167,0.001544882,0.004498446,0.003025722,0.0008457752],"domain_scores_gemma":[0.9605447,0.01493367,0.001178941,0.01521213,0.00732732,0.0008031309],"domain_codex":null,"domain_gemma":"reproducibility","domain_candidate":"reproducibility","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002603501,0.0000525357,0.001678646,0.001014365,0.0002056593,0.00006425455,0.00009209677,0.000674044,0.001721539,0.001556869,0.9803484,0.01233131],"study_design_scores_gemma":[0.0009521787,0.0000860052,0.004723562,0.0004513102,0.0003278408,0.0003364729,0.0001314987,0.00551405,0.008733306,0.01188325,0.9667226,0.0001379294],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.003496935,0.001388986,0.04805792,0.001805647,0.00408926,0.0008202401,0.8813419,0.05191652,0.007082591],"genre_scores_gemma":[0.007772376,0.000459917,0.05367112,0.001166565,0.0005183298,0.00364216,0.9111287,0.01586353,0.005777294],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9814584,"threshold_uncertainty_score":0.2048855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1284069281114279,"score_gpt":0.3375719947260374,"score_spread":0.2091650666146095,"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."}}