{"id":"W4413956711","doi":"10.1088/2632-2153/ae6416","title":"Multi-task Attention for Doped Thermoelectric Properties Prediction","year":2025,"lang":"en","type":"preprint","venue":"Machine Learning Science and Technology","topic":"Advanced Thermoelectric Materials and Devices","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Army Research Office; Nanyang Technological University","keywords":"Thermoelectric effect; Task (project management); Doping; Materials science; Thermoelectric materials; Computer science; Engineering physics; Optoelectronics; Physics; Engineering; Systems engineering; Thermodynamics","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":[0.00122228,0.002081183,0.0009648657,0.00100627,0.0006009394,0.0009336607,0.002119559,0.002425511,0.003712427],"category_scores_gemma":[0.003078648,0.0004839844,0.0009902739,0.0008752192,0.0006012033,0.001896776,0.0009856687,0.001745402,0.001197959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00163707,"about_ca_system_score_gemma":0.001114038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0122326,"about_ca_topic_score_gemma":0.01900348,"domain_scores_codex":[0.999607,0.00009452461,0.00001269084,0.000174031,0.0000508846,0.00006083494],"domain_scores_gemma":[0.999014,0.0005408764,0.00005457276,0.0001241343,0.0002025436,0.00006383014],"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":[0.00120853,0.0009357665,0.00691818,0.000599522,0.0003172338,0.0004237115,0.0001075171,0.6269608,0.01270389,0.004591735,0.05069377,0.2945393],"study_design_scores_gemma":[0.0000255587,0.0000438725,0.0003137752,0.000009338456,0.00002020146,0.00002129134,0.00001309126,0.9932166,0.002758151,0.002424615,0.001147331,0.000006317035],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.666254,0.008524745,0.2703981,0.004954329,0.001315427,0.000342835,0.008143286,0.02083912,0.0192281],"genre_scores_gemma":[0.9011626,0.0005955345,0.07255828,0.001110116,0.0003031609,0.00016662,0.01091471,0.0003371461,0.01285182],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0122326,"threshold_uncertainty_score":0.02432281,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01439925231097692,"score_gpt":0.2648935638034858,"score_spread":0.2504943114925089,"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."}}