{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":1,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":1,"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":"54dd16143ed9","filters":{"venue":"World Journal of Materials Science"}},"results":[{"id":"W4391042413","doi":"10.61784/wjms240168","title":"APPLICATION PROGRESS OF MATERIALS GENOME TECHNOLOGY IN THE FIELD OF NEW ENERGY MATERIALS","year":2024,"lang":"en","type":"article","venue":"World Journal of Materials Science","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Throughput; Big data; Computer science; Field (mathematics); Characterization (materials science); Data science; Nanotechnology; Systems engineering; Engineering; Materials science; Data mining","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.00841346656735523,"gpt":0.297573559612996,"spread":0.2891600930456408,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.01027321,0.0003090667,0.000904587,0.001603771,0.000138823,0.0005829585,0.003291706,0.0001241441,0.001551739],"category_scores_gemma":[0.0006651874,0.0002065512,0.00007179422,0.002623479,0.001359596,0.0008635651,0.0004116143,0.0001531379,0.0000315998],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000961218,"about_ca_system_score_gemma":0.0006788137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003644963,"about_ca_topic_score_gemma":0.00002509971,"domain_scores_codex":[0.9948772,0.0004583987,0.002177604,0.0005496274,0.00131224,0.0006248862],"domain_scores_gemma":[0.9968241,0.00034898,0.001606732,0.0007549562,0.0003541411,0.0001111026],"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":[0.0001336847,0.00006761561,0.00007139667,0.0002183132,0.000005737641,0.00002751211,0.0006037123,0.0002145391,0.9810745,0.01670004,0.0002328664,0.000650081],"study_design_scores_gemma":[0.0002337702,0.0003070931,0.001339407,0.0004443633,0.00002364982,0.0001886329,0.000120268,0.00001776462,0.9873205,0.007787179,0.002019794,0.0001976207],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9906765,0.0007592411,0.001941565,0.003164815,0.002912178,0.0003256455,0.00005653587,0.00004696112,0.0001166093],"genre_scores_gemma":[0.993378,0.00008147859,0.005815367,0.0001550873,0.0004171152,0.00002653223,0.000002999644,0.0000256245,0.00009781881],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009608024,"threshold_uncertainty_score":0.999361,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}