{"id":"W4409405484","doi":"10.1007/s10664-025-10651-z","title":"Predicting the understandability of computational notebooks through code metrics analysis","year":2025,"lang":"en","type":"article","venue":"Empirical Software Engineering","topic":"Software Engineering Research","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Vector Institute","funders":"","keywords":"Computer science; Programming language; Code (set theory)","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":[0.001375311,0.0005328371,0.0002392095,0.00366036,0.0002773339,0.001303414,0.0004591406,0.0006452331,0.001169799],"category_scores_gemma":[0.04871589,0.0002050157,0.0003608265,0.002330319,0.000348203,0.002271383,0.0006280015,0.0006822919,0.0005096709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007374247,"about_ca_system_score_gemma":0.0006207707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008177218,"about_ca_topic_score_gemma":0.01172124,"domain_scores_codex":[0.9987047,0.0003083187,0.0001021916,0.0002254568,0.000583606,0.00007572612],"domain_scores_gemma":[0.9527327,0.03081341,0.006995219,0.002617789,0.006072269,0.0007686264],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005330917,0.0004741016,0.6691598,0.0002905037,0.0001975878,0.0003175203,0.001443113,0.0476392,0.01891554,0.002233942,0.002973048,0.2558225],"study_design_scores_gemma":[0.00003951288,0.000847526,0.507492,0.0000915329,0.0001085328,0.0003021731,0.0009135983,0.4593406,0.02247947,0.004844367,0.003462488,0.00007831971],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9898105,0.0001160709,0.008227631,0.00007820759,0.000006786249,0.00002822339,0.0005652657,0.0003418314,0.0008255159],"genre_scores_gemma":[0.9874846,0.00008390733,0.01001048,0.0000138297,0.000006196547,0.00002654911,0.001490752,0.00009305151,0.000790629],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9986247,"threshold_uncertainty_score":0.01625925,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03729922534070134,"score_gpt":0.3189262083606235,"score_spread":0.2816269830199222,"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."}}