{"id":"W2567860882","doi":"","title":"Lucx: lucid enriched with context","year":2006,"lang":"en","type":"dissertation","venue":"","topic":"Model-Driven Software Engineering Techniques","field":"Computer Science","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Programming language; Context (archaeology); Syntax; Semantics (computer science); Class (philosophy); Functional logic programming; Programming paradigm; Artificial intelligence; Inductive programming","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009299921,0.0003412432,0.0003216337,0.0002015496,0.00004838581,0.0001378207,0.001028877,0.000251971,0.00002027433],"category_scores_gemma":[0.000002535264,0.0002821657,0.00007620172,0.0002890123,0.00001264624,0.0002486952,0.00005026296,0.0002877865,0.00004150704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007153689,"about_ca_system_score_gemma":0.0001142109,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003515463,"about_ca_topic_score_gemma":0.0003526326,"domain_scores_codex":[0.9985216,0.00001865267,0.0002494979,0.0005493893,0.0003840179,0.0002768067],"domain_scores_gemma":[0.9987568,0.00002961307,0.0001314135,0.000833997,0.0001798479,0.0000683255],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002137586,0.00009654121,0.0001187368,0.0001724831,0.00007835992,0.00007490892,0.0007405131,0.0003449149,0.000677779,0.7479694,0.03730389,0.2124011],"study_design_scores_gemma":[0.001775626,0.001011448,0.01525195,0.0009964083,0.0001471572,0.000107821,0.00006820678,0.07917444,0.06429396,0.009940124,0.8215619,0.005670942],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001741272,0.0001691975,0.9800082,0.000041121,0.0002907971,0.0002844727,0.000001765083,0.002491024,0.01497215],"genre_scores_gemma":[0.1020445,0.0000124993,0.8484017,0.0001455048,0.00007624779,0.0001489741,0.0002790281,0.0000677077,0.04882381],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.784258,"threshold_uncertainty_score":0.999963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00493524456319461,"score_gpt":0.2086582575013212,"score_spread":0.2037230129381266,"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."}}