{"id":"W2342073150","doi":"10.1108/jdoc-09-2015-0111","title":"On the composition of scientific abstracts","year":2016,"lang":"en","type":"article","venue":"Journal of Documentation","topic":"Advanced Text Analysis Techniques","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"","keywords":"Sentence; Computer science; Rhetorical question; Argumentation theory; Relation (database); Information retrieval; Originality; Similarity (geometry); Scientific writing; Composition (language); Value (mathematics); Linguistics; Natural language processing; Artificial intelligence; Sociology; Qualitative research","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":[0.02917601,0.0008729821,0.001131334,0.02338853,0.00435795,0.009041313,0.001215223,0.001262507,0.006679942],"category_scores_gemma":[0.2383729,0.0007777446,0.001062503,0.01746222,0.002450177,0.008899843,0.004481961,0.001478051,0.003218162],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002926964,"about_ca_system_score_gemma":0.004363654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001095365,"about_ca_topic_score_gemma":0.000777865,"domain_scores_codex":[0.9381936,0.02313746,0.01077302,0.004856013,0.02227841,0.0007614921],"domain_scores_gemma":[0.711122,0.15962,0.04124778,0.01436244,0.06935618,0.004291654],"domain_codex":null,"domain_gemma":"reporting","domain_candidate":"reporting","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001896522,0.000229913,0.04911057,0.00980584,0.0005174818,0.00218844,0.02937195,0.004388246,0.04107347,0.09136626,0.03846021,0.7315911],"study_design_scores_gemma":[0.0002823569,0.001145703,0.1222319,0.004459243,0.001055796,0.006165283,0.01901329,0.03544673,0.03834672,0.195836,0.5755493,0.0004676744],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4047607,0.02610855,0.4292643,0.01131479,0.005802013,0.004534266,0.01213252,0.003900565,0.1021824],"genre_scores_gemma":[0.6357809,0.006105417,0.3251061,0.00121514,0.002731586,0.002131162,0.01250298,0.001533268,0.01289348],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9766115,"threshold_uncertainty_score":0.1542993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01361670198581772,"score_gpt":0.3029858504638016,"score_spread":0.2893691484779839,"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."}}