{"id":"W4402759396","doi":"10.3390/cryst14100830","title":"Machine Learning to Predict Workability and Compressive Strength of Low- and High-Calcium Fly Ash–Based Geopolymers","year":2024,"lang":"en","type":"article","venue":"Crystals","topic":"Concrete and Cement Materials Research","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Université du Québec à Chicoutimi","funders":"Institut Teknologi Bandung","keywords":"Fly ash; Compressive strength; Geopolymer; High calcium; Low calcium; Materials science; Calcium; Composite material; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001032558,0.0005502152,0.0004198742,0.0006376274,0.0001427148,0.0003763883,0.0003322978,0.0005456445,0.0003692422],"category_scores_gemma":[0.001556632,0.0002450011,0.0004523758,0.0005216577,0.0001924843,0.0004046269,0.0001791065,0.0004624953,0.0001322872],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005229269,"about_ca_system_score_gemma":0.0005049601,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005397802,"about_ca_topic_score_gemma":0.007180256,"domain_scores_codex":[0.9997484,0.00006251362,0.00001807504,0.0000532082,0.00009086704,0.00002690285],"domain_scores_gemma":[0.9992588,0.0004281531,0.0001328514,0.00003752899,0.0001225908,0.00002007721],"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":[0.0001730102,0.0003306531,0.01793625,0.0001004093,0.00007376551,0.00005687673,0.00002103952,0.8711179,0.06611457,0.0002023375,0.0001377225,0.04373545],"study_design_scores_gemma":[0.000004105279,0.00007071312,0.005385885,0.000004200818,0.000009756591,0.000007412448,0.00000455073,0.9747074,0.01964349,0.0000724571,0.00008295886,0.000007044586],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9452404,0.0002633982,0.05330826,0.00004612408,0.00001581872,0.00003006006,0.0001419423,0.0002771827,0.0006768124],"genre_scores_gemma":[0.9927056,0.00006027112,0.006850017,0.000006845618,0.000002084704,0.00001276015,0.00009377876,0.000005288834,0.0002632093],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005397802,"threshold_uncertainty_score":0.01073277,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01105460440947801,"score_gpt":0.246877208260511,"score_spread":0.235822603851033,"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."}}