{"id":"W4412191134","doi":"10.1007/s11831-025-10310-y","title":"Computational Modeling of Indoor Organic Photovoltaics: Dataset Curation, Predictive Analysis, and Machine Learning Approaches","year":2025,"lang":"en","type":"article","venue":"Archives of Computational Methods in Engineering","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"National Yang Ming Chiao Tung University; National Science and Technology Council; Ministry of Education, India; Shanghai Educational Development Foundation","keywords":"Computer science; Photovoltaics; Machine learning; Artificial intelligence; Organic solar cell; Predictive modelling; Photovoltaic system; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.001270501,0.000149825,0.0003936352,0.000763361,0.00006574632,0.00003265946,0.0002569984,0.00003565552,0.00002779538],"category_scores_gemma":[0.0008527123,0.0001544107,0.00004483455,0.0007566677,0.0001350794,0.0002098082,0.000206153,0.0001798171,3.783707e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001693772,"about_ca_system_score_gemma":0.00007622839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007791404,"about_ca_topic_score_gemma":0.000005228191,"domain_scores_codex":[0.9982705,0.0003831623,0.0006149911,0.0003350743,0.0002357314,0.000160599],"domain_scores_gemma":[0.9975557,0.002016018,0.0002067023,0.0001296124,0.00005455545,0.0000374604],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000321632,0.00002960502,0.005743301,0.0001614451,0.00005744809,3.926517e-7,0.0005351522,0.9283774,0.0618578,0.002514439,4.072564e-7,0.0006903951],"study_design_scores_gemma":[0.0002620438,0.00002978249,0.01207565,0.00008157282,0.00005534279,0.000001972366,0.00005211959,0.9638342,0.009703817,0.01379082,0.000005294439,0.0001073708],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1499181,0.0001698148,0.8494956,0.00002602651,0.00006386136,0.000123343,0.0001214897,0.00002839035,0.00005332727],"genre_scores_gemma":[0.504859,0.000004737629,0.4949363,0.000005399661,0.000006112028,0.000006644157,0.0001742827,0.00000532018,0.00000212094],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.354941,"threshold_uncertainty_score":0.6296684,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02452032047835891,"score_gpt":0.3113933696814965,"score_spread":0.2868730492031376,"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."}}