{"id":"W2905295290","doi":"","title":"Studying Uncertainty in Science: a distributional analysis through the IMRaD structure","year":2018,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Data 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","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.0085998,0.0008284599,0.002155439,0.004949336,0.001404683,0.004851831,0.002367032,0.002618429,0.007625767],"category_scores_gemma":[0.03617951,0.0005488516,0.002090824,0.004217676,0.00501002,0.01114545,0.005032194,0.004561597,0.0007451249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002105844,"about_ca_system_score_gemma":0.0009584087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001042793,"about_ca_topic_score_gemma":0.000575865,"domain_scores_codex":[0.9955527,0.002337432,0.0002482626,0.0006724694,0.00089532,0.0002937286],"domain_scores_gemma":[0.9676666,0.02128015,0.003958318,0.003765017,0.001864249,0.001465733],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00003162551,0.00002902447,0.001584855,0.00008313782,0.00005974798,0.00008184397,0.000186331,0.02198594,0.0003638174,0.9594079,0.0008791498,0.01530653],"study_design_scores_gemma":[0.000006254295,0.00004153265,0.0007186455,0.00003772398,0.00002114988,0.00008372833,0.00008638434,0.08094942,0.0001680615,0.9160993,0.001772148,0.00001567987],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08255983,0.002729453,0.8878983,0.007515742,0.0002026081,0.00005475223,0.0004042877,0.000197436,0.01843759],"genre_scores_gemma":[0.908929,0.003794526,0.07627872,0.0006971535,0.001173751,0.0001712598,0.0004309403,0.0001620243,0.008362608],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9950507,"threshold_uncertainty_score":0.04548067,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05853507489326345,"score_gpt":0.343857287016833,"score_spread":0.2853222121235696,"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."}}