{"id":"W2322850719","doi":"10.1039/c5tc04339e","title":"Understanding thermoelectric properties from high-throughput calculations: trends, insights, and comparisons with experiment","year":2016,"lang":"en","type":"article","venue":"Journal of Materials Chemistry C","topic":"Advanced Thermoelectric Materials and Devices","field":"Materials Science","cited_by":263,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Basic Energy Sciences; U.S. Department of Energy","keywords":"Materials science; Thermoelectric effect; Throughput; Thermoelectric materials; Engineering physics; Nanotechnology; Computer science; Thermodynamics; Engineering; Physics","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":[0.0006437939,0.001260956,0.0007203264,0.0006176585,0.0003976526,0.000966147,0.001121186,0.0005980945,0.003090723],"category_scores_gemma":[0.001408255,0.0006331423,0.0006576678,0.001403104,0.000308361,0.002322911,0.0004198454,0.001316447,0.001093193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006729416,"about_ca_system_score_gemma":0.0006544811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001674208,"about_ca_topic_score_gemma":0.002239231,"domain_scores_codex":[0.9997836,0.00002614501,0.000009606038,0.00002683868,0.00013065,0.00002314412],"domain_scores_gemma":[0.9994808,0.0002416356,0.00003153036,0.00009074375,0.000142026,0.0000132939],"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":[0.0004126403,0.0003392589,0.007067817,0.003462674,0.0004355471,0.0002455268,0.0002565418,0.6617482,0.1395721,0.0364822,0.0228847,0.1270928],"study_design_scores_gemma":[0.00006937589,0.0001402303,0.004087011,0.0002155591,0.0001878708,0.0001028336,0.0000781364,0.7915176,0.1561109,0.02371708,0.02369392,0.00007950626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5151262,0.04607838,0.3225414,0.002803125,0.0003273668,0.0002133535,0.01937374,0.007691576,0.08584487],"genre_scores_gemma":[0.8821778,0.02423327,0.07853325,0.0001828481,0.000103572,0.0003097258,0.009362118,0.001328232,0.003769098],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003090723,"threshold_uncertainty_score":0.01033944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04946430380782477,"score_gpt":0.242874762867185,"score_spread":0.1934104590593602,"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."}}