{"id":"W6948946464","doi":"10.5281/zenodo.10028455","title":"Analyse de l'environnement pour le programme de bourses de doctorat du CRSH","year":2006,"lang":"fr","type":"report","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Research Data Management Practices","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Human science; Population; Research methodology; Statistical analysis","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01235878,0.0002513741,0.0006839174,0.002900764,0.004652486,0.005017047,0.001252298,0.0005607276,0.009580935],"category_scores_gemma":[0.03142207,0.0004174204,0.0005072712,0.006773843,0.002131677,0.001160166,0.003055393,0.001500488,0.001001943],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04506494,"about_ca_system_score_gemma":0.1004296,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7503288,"about_ca_topic_score_gemma":0.7857583,"domain_scores_codex":[0.983395,0.006364254,0.0005235,0.001167345,0.006129684,0.002420111],"domain_scores_gemma":[0.96432,0.01087701,0.003044609,0.001447469,0.01348463,0.006826213],"domain_codex":null,"domain_gemma":"incentives","domain_candidate":"incentives","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005374335,0.0003989767,0.4475618,0.001553035,0.0001985292,0.0004200492,0.1455527,0.002646738,0.001662159,0.02021777,0.02017468,0.3590761],"study_design_scores_gemma":[0.00002234885,0.000260573,0.8696945,0.0005756818,0.00004256569,0.0000392294,0.05764048,0.0007793043,0.0005029934,0.000707096,0.06968289,0.00005229712],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9284948,0.001939606,0.005072777,0.009939601,0.0002824624,0.001007403,0.005295476,0.0002321278,0.04773576],"genre_scores_gemma":[0.9579437,0.001227612,0.005712442,0.0006541943,0.00007250839,0.000746017,0.001512773,0.0001016165,0.03202919],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9876412,"threshold_uncertainty_score":0.5022832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1323536741127737,"score_gpt":0.3185552929095935,"score_spread":0.1862016187968198,"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."}}