{"id":"W4296449928","doi":"10.3390/gels8090592","title":"A Molecular Description of Hydrogel Forming Polymers for Cement-Based Printing Paste Applications","year":2022,"lang":"en","type":"article","venue":"Gels","topic":"Innovations in Concrete and Construction Materials","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Collège militaire royal du Canada; Oak Ridge National Laboratory; Basic Energy Sciences; National Science Foundation; Division of Civil, Mechanical and Manufacturing Innovation; U.S. Department of Energy; Office of Science","keywords":"Self-healing hydrogels; Differential scanning calorimetry; Materials science; Polymer; Neutron scattering; Neutron spectroscopy; Radius of gyration; Viscosity; Neutron diffraction; Small-angle neutron scattering; Cement; Chemical engineering; Spectroscopy; Composite material; Polymer chemistry; Scattering; Chemistry; Crystallography; Thermodynamics; Optics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001170311,0.00005735195,0.0000775252,0.0001062668,0.000120351,0.00001123334,0.00007512705,0.00001705577,0.00009715391],"category_scores_gemma":[0.000004646759,0.00007301463,0.00003944324,0.0001951366,0.00001321916,0.00004840337,0.00002099669,0.00003760005,0.000001245667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004587229,"about_ca_system_score_gemma":0.00001875384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003747987,"about_ca_topic_score_gemma":2.115531e-7,"domain_scores_codex":[0.9994976,0.000007111984,0.0002168698,0.00008059017,0.00008534919,0.0001124684],"domain_scores_gemma":[0.9997525,0.00001395972,0.00005686067,0.0001283768,0.00003737876,0.00001098868],"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":[0.000006398354,0.000005913744,0.00008198498,0.0001007708,0.00003207269,2.016241e-7,0.00007434522,0.03688207,0.9283746,0.01438639,0.00004180708,0.02001341],"study_design_scores_gemma":[0.0004715268,0.00003221511,0.00001004734,0.00001111152,0.0000296799,0.000004483719,0.000527085,0.0464798,0.9242656,0.001636714,0.02636952,0.0001622209],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6536347,0.00003944635,0.3446389,0.00002056229,0.0001883683,0.0003691411,0.00004732131,0.00008652002,0.0009749942],"genre_scores_gemma":[0.9929836,6.567687e-7,0.005994597,0.00005433482,0.00002947251,0.0008555657,0.0000420613,0.00001590047,0.00002380249],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3393489,"threshold_uncertainty_score":0.297745,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01364598234896877,"score_gpt":0.2169807600564561,"score_spread":0.2033347777074873,"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."}}