{"id":"W4220803401","doi":"10.1007/s40037-022-00708-w","title":"Writing for the reader: Using reader expectation principles to maximize clarity","year":2022,"lang":"en","type":"article","venue":"Perspectives on Medical Education","topic":"Discourse Analysis in Language Studies","field":"Arts and Humanities","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"CLARITY; Computer science; Data science; Information retrieval; Chemistry","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":"codex-gemma-dda1882f352a","candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006436884,0.0001134743,0.0001434947,0.0001275874,0.001497472,0.0001079986,0.0002343087,0.00001794005,0.003948728],"category_scores_gemma":[0.001687585,0.00008563426,0.00009925989,0.00009517382,0.0002053724,0.00009864483,0.00008415468,0.0002357167,0.00000865646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002816922,"about_ca_system_score_gemma":0.0002787497,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005546194,"about_ca_topic_score_gemma":0.0003789406,"domain_scores_codex":[0.9985731,0.0001182851,0.0002197158,0.0003039305,0.0005940378,0.0001909232],"domain_scores_gemma":[0.9990408,0.0003617234,0.0001020413,0.0002222744,0.0002008854,0.00007226749],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00005951956,0.0005353662,0.0001836019,0.00001500968,0.0001417436,8.086122e-7,0.6371484,0.0002971965,0.00001325607,0.3434044,0.005736892,0.01246382],"study_design_scores_gemma":[0.0001428368,0.00006631215,0.0006641392,0.00003071407,0.00008719818,0.000002385577,0.9541283,0.001373017,0.000008553434,0.0007934474,0.04257927,0.0001238605],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.702208,0.00987031,0.00890794,0.1524405,0.005709623,0.003374006,0.0001301409,0.0003645388,0.116995],"genre_scores_gemma":[0.9918513,0.00003919743,0.0008774583,0.002265715,0.002163609,0.0006370872,0.00002655531,0.00002167012,0.002117367],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3426109,"threshold_uncertainty_score":0.9998025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07695805903273356,"score_gpt":0.3659429267976809,"score_spread":0.2889848677649473,"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."}}