{"id":"W2753133191","doi":"10.37514/jwa-j.2017.1.1.08","title":"Discovering the Predictive Power of Five Baseline Writing Competences","year":2017,"lang":"en","type":"article","venue":"The Journal of Writing Analytics","topic":"Text Readability and Simplification","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Athabasca University","funders":"","keywords":"Baseline (sea); Benchmark (surveying); Computer science; Credibility; Consistency (knowledge bases); Constructive; State (computer science); Artificial intelligence; Data science; Machine learning; Programming language","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":[0.007583483,0.001023759,0.0006778386,0.005290977,0.00057665,0.003091597,0.0007699314,0.0009619975,0.003248837],"category_scores_gemma":[0.05633528,0.0002068342,0.0007374623,0.002645255,0.0007251812,0.00194687,0.001538588,0.001837347,0.002005578],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006354713,"about_ca_system_score_gemma":0.0007324161,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003992356,"about_ca_topic_score_gemma":0.003580199,"domain_scores_codex":[0.9962673,0.001073073,0.0003343032,0.001251567,0.0008921959,0.0001816835],"domain_scores_gemma":[0.9365733,0.04658729,0.004636703,0.003726781,0.006618037,0.001857828],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004348135,0.0002551531,0.8695667,0.0004006439,0.0004234747,0.0001454826,0.0005874438,0.00606304,0.001606314,0.0006177677,0.004652784,0.1152464],"study_design_scores_gemma":[0.00007744453,0.0006175255,0.8315547,0.0003217802,0.0003525961,0.0003282626,0.0008693569,0.1507775,0.00450674,0.00432456,0.006181357,0.00008807433],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9708571,0.002124819,0.01445987,0.0004475091,0.0001383817,0.0001129046,0.004647925,0.0005687362,0.006642719],"genre_scores_gemma":[0.9924033,0.0001931311,0.003020692,0.00002453242,0.00004666522,0.00005905701,0.003765268,0.0000314862,0.0004559142],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007583483,"threshold_uncertainty_score":0.04010576,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02459756427332719,"score_gpt":0.2848382890596801,"score_spread":0.2602407247863529,"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."}}