{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":2,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":2,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12","author_layer_release":"2026-06-26"},"query_hash":"996b596d218f","filters":{"venue":"Computational Materials Today"}},"results":[{"id":"W4401486285","doi":"10.1016/j.commt.2024.100009","title":"Unveiling Novel Direct Bandgap Allotropes of Germanium: A Computational Exploration","year":2024,"lang":"en","type":"article","venue":"Computational Materials Today","topic":"Nanowire Synthesis and Applications","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; University of Saskatchewan","keywords":"Germanium; Band gap; Diamond; Diamond cubic; Materials science; Direct and indirect band gaps; Semiconductor; Electronic band structure; Brillouin zone; Atom (system on chip); Silicon; Germanium compounds; Condensed matter physics; Crystallography; Optoelectronics; Chemistry; Physics; Computer science","authors":[{"name":"Mangladeep Bhullar","is_ca":true},{"name":"Akinwumi Akinpelu","is_ca":true},{"name":"Yansun Yao","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01859288205314336,"gpt":0.2398956408008782,"spread":0.2213027587477349,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001706437,0.0001404789,0.0001997441,0.0001210723,0.00005466307,0.0001098078,0.00009463749,0.00004908356,0.0002759668],"category_scores_gemma":[0.000007991426,0.0001373153,0.00005477482,0.0001662824,0.00003337392,0.0002435265,0.00002083118,0.00004187536,0.0001550691],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000379869,"about_ca_system_score_gemma":0.00004191381,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005694777,"about_ca_topic_score_gemma":7.436667e-7,"domain_scores_codex":[0.9990555,0.00002132238,0.0004074281,0.000183808,0.0002072583,0.0001246529],"domain_scores_gemma":[0.9995342,0.0002218919,0.00003982829,0.00008678595,0.00007932038,0.00003800952],"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":[0.000004602132,0.00003161519,0.000003553469,0.0001767779,0.00005994696,0.000001310948,0.0001595342,0.7807804,0.1860722,0.03008613,0.00164325,0.0009805954],"study_design_scores_gemma":[0.0004679402,0.00004340518,0.003203531,0.0004567611,0.00007678784,0.00002452891,0.00004781815,0.6549839,0.2955358,0.021911,0.02262691,0.0006215983],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3549991,0.0008101495,0.636631,0.00039952,0.001196609,0.0003842384,0.0004899293,0.0007973085,0.004292149],"genre_scores_gemma":[0.9887143,0.00001982643,0.01056783,0.00002120288,0.0002091206,0.00005961967,0.0003073871,0.00003832251,0.00006233666],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6337153,"threshold_uncertainty_score":0.5599557,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4401812118","doi":"10.1016/j.commt.2024.100011","title":"Data-driven integration of synthetic representative volume elements and machine learning for improved microstructure-property linkage and material performance in ceramics","year":2024,"lang":"en","type":"article","venue":"Computational Materials Today","topic":"Advanced ceramic materials synthesis","field":"Materials Science","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Linkage (software); Microstructure; Property (philosophy); Ceramic; Volume (thermodynamics); Materials science; Artificial intelligence; Computer science; Metallurgy; Biology; Physics; Genetics; Thermodynamics","authors":[{"name":"Mohammad Rezasefat","is_ca":true},{"name":"James D. Hogan","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0252016665971674,"gpt":0.283239182528799,"spread":0.2580375159316316,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000745014,0.0002409658,0.0004292148,0.0001331602,0.0001093376,0.0002940008,0.0002327028,0.00009130037,0.0003049219],"category_scores_gemma":[0.0002152779,0.0001851642,0.00001860296,0.00008266332,0.0001814446,0.0005561594,0.0003149717,0.00007455106,0.000006689736],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005744045,"about_ca_system_score_gemma":0.00005634887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008044458,"about_ca_topic_score_gemma":0.00001240543,"domain_scores_codex":[0.9980114,0.0001934556,0.000741469,0.0006524706,0.0001493713,0.0002518285],"domain_scores_gemma":[0.9991516,0.000241123,0.0002415238,0.0002261559,0.00009356592,0.0000460696],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000267327,0.00001766602,0.0001654211,0.0004938261,0.00001546784,0.000002166681,0.0004402212,0.001927553,0.9934224,0.0002181019,0.00001905958,0.003010757],"study_design_scores_gemma":[0.0007138723,0.0001950471,0.005805527,0.0004256035,0.00004947945,0.00004190409,0.0001123524,0.316671,0.6714533,0.003661528,0.0005124841,0.0003578471],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9847946,0.00009432476,0.0103018,0.0001940157,0.0008863861,0.0006789098,0.002972245,0.00006961602,0.000008110842],"genre_scores_gemma":[0.9610296,0.00008318258,0.03732538,0.00002369662,0.0001238159,0.00005900892,0.001220804,0.00004115794,0.00009339335],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3219691,"threshold_uncertainty_score":0.7550775,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}