{"id":"W4409367598","doi":"10.1007/s43452-025-01190-x","title":"Advancing coal gangue-based alkali-activated materials development through innovative data augmentation and machine learning strategies","year":2025,"lang":"en","type":"article","venue":"Archives of Civil and Mechanical Engineering","topic":"Concrete and Cement Materials Research","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Structural material; Gangue; Coal; Construction engineering; Materials science; Engineering; Process engineering; Waste management; Metallurgy","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":[0.0001808881,0.0001592236,0.0002447363,0.0001224755,0.00005578045,0.0000564172,0.000129102,0.00003873849,0.00003224103],"category_scores_gemma":[0.0000630286,0.0001560742,0.000009696381,0.0001450039,0.00002758271,0.0002375629,0.0002024763,0.0001227953,3.575428e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000132666,"about_ca_system_score_gemma":0.00004693575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000356147,"about_ca_topic_score_gemma":0.00002650379,"domain_scores_codex":[0.9991311,0.00002914182,0.0002952799,0.0002054146,0.0001099412,0.0002290507],"domain_scores_gemma":[0.999592,0.0001758815,0.00003237738,0.0001411164,0.00001800084,0.00004064102],"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":[0.00005397139,0.000007695658,0.00001625608,0.0006247688,0.00008186021,0.000002172205,0.0002525567,0.008815727,0.9832015,0.001965936,0.000006455665,0.004971116],"study_design_scores_gemma":[0.0006220436,0.00004608019,0.0003572255,0.0003185018,0.00001306102,0.000001132206,0.0001882184,0.09629171,0.9003008,0.0002109689,0.001491798,0.0001584353],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.641884,0.0004322841,0.3566122,0.00004071934,0.0001116722,0.0001936917,0.00003502424,0.0001449723,0.0005453778],"genre_scores_gemma":[0.9937377,0.0002470877,0.005749027,0.0000149379,0.00001661918,0.00001800252,0.0001847055,0.00001841251,0.00001351772],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3518537,"threshold_uncertainty_score":0.6364521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01825699830410437,"score_gpt":0.2611876857463557,"score_spread":0.2429306874422513,"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."}}