{"id":"W4402231783","doi":"10.26434/chemrxiv-2024-wqd2m","title":"Programmatic Data Analysis for Quantitative Isothermal Heat Flow Calorimetry of Cementitious Materials","year":2024,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"thermodynamics and calorimetric analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"inVentiv Health Clinical","funders":"","keywords":"Isothermal titration calorimetry; Cementitious; Calorimetry; Isothermal process; Tartaric acid; Materials science; Cement; Chemistry; Thermodynamics; Organic chemistry; Biochemistry; Composite material","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004952152,0.0004653055,0.00125212,0.0004453429,0.00005665517,0.0001884421,0.001211994,0.0004174293,0.001446504],"category_scores_gemma":[0.0001821033,0.0004292558,0.0006089633,0.0007814717,0.0001389532,0.00005005871,0.001617734,0.0003373175,0.00002571706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001146431,"about_ca_system_score_gemma":0.0001739387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005215743,"about_ca_topic_score_gemma":0.00003798848,"domain_scores_codex":[0.997252,0.00002769367,0.0009072641,0.001055053,0.0003769231,0.0003811018],"domain_scores_gemma":[0.9971346,0.0002038408,0.0003688984,0.001987393,0.0002060386,0.00009920352],"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":[0.0006563101,0.001668123,0.001086038,0.04618619,0.08601727,0.00006503424,0.001271896,0.005632201,0.8384044,0.009285408,0.003163027,0.006564107],"study_design_scores_gemma":[0.001248471,0.0001101697,0.000102318,0.001027863,0.03478995,0.000003324022,0.000842851,0.3992751,0.5339857,0.02534251,0.00137809,0.001893738],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9289476,0.006540748,0.05254779,0.0001082925,0.0007968488,0.000665442,0.007292115,0.0002498201,0.002851338],"genre_scores_gemma":[0.9759974,0.0001444066,0.01262105,0.00001196245,0.0002958734,0.0001752581,0.009723434,0.0001029183,0.0009276277],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3936429,"threshold_uncertainty_score":0.9998159,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06635383572079022,"score_gpt":0.3350751926748313,"score_spread":0.2687213569540411,"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."}}