{"id":"W2057792061","doi":"10.1145/1621995.1622036","title":"Probing the use of charts and graphs in technical documentation through analysis and pragmatic collaboration","year":2009,"lang":"en","type":"article","venue":"","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"IBM (Canada)","funders":"","keywords":"Documentation; Computer science; Terminology; Graphics; Comprehension; Technical documentation; Software documentation; User analysis; Search engine indexing; Technical writing; World Wide Web; Information retrieval; Visualization; Multimedia; Human–computer interaction; Artificial intelligence; Software; Software development; Programming language; Linguistics","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":[],"consensus_categories":[],"category_scores_codex":[0.05478003,0.001139002,0.0007846446,0.01170339,0.006073391,0.01433531,0.002226091,0.002280696,0.00382298],"category_scores_gemma":[0.2256192,0.0007872889,0.0005349238,0.007897815,0.01140738,0.01682766,0.009409656,0.003431202,0.0006873548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005680777,"about_ca_system_score_gemma":0.005757552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002498119,"about_ca_topic_score_gemma":0.00320959,"domain_scores_codex":[0.8656862,0.1143295,0.003618802,0.003212735,0.01153495,0.001617726],"domain_scores_gemma":[0.7697383,0.1842069,0.01535792,0.01283118,0.0160228,0.001842935],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001511749,0.0001416078,0.008470866,0.0009133357,0.00003208705,0.0006420964,0.7469307,0.0008873856,0.002619501,0.1288905,0.00498335,0.1053374],"study_design_scores_gemma":[0.00009565449,0.000347167,0.01109872,0.002264844,0.00006852201,0.001586472,0.6393537,0.009631595,0.00528285,0.2164019,0.1136499,0.0002187647],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4024365,0.00333159,0.4778855,0.01303941,0.0004189396,0.0019218,0.0005164193,0.001302504,0.09914724],"genre_scores_gemma":[0.8827666,0.0008589541,0.1116165,0.0005673767,0.00006909777,0.0009290317,0.0001279938,0.0003404541,0.002723995],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05478003,"threshold_uncertainty_score":0.2897079,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02604512500146801,"score_gpt":0.3209403146490181,"score_spread":0.2948951896475501,"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."}}