{"id":"W2283856879","doi":"10.1039/c5nr08142d","title":"Interaction grand potential between calcium–silicate–hydrate nanoparticles at the molecular level","year":2016,"lang":"en","type":"article","venue":"Nanoscale","topic":"Clay minerals and soil interactions","field":"Materials Science","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hatch (Canada)","funders":"","keywords":"Calcium silicate hydrate; Silicate; Calcium silicate; Hydrate; Nanoparticle; Calcium; Chemical engineering; Materials science; Chemistry; Mineralogy; Nanotechnology; Metallurgy; Engineering; Organic chemistry","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002019264,0.0001434551,0.0001577564,0.00003609652,0.0003142135,0.00009124877,0.0002224341,0.00007315478,0.001224375],"category_scores_gemma":[0.00006078904,0.00007664289,0.0001118073,0.00009481665,0.0001601952,0.0002508797,0.0001340445,0.00007608475,0.001537762],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009056002,"about_ca_system_score_gemma":0.0000207993,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000262731,"about_ca_topic_score_gemma":0.0002351521,"domain_scores_codex":[0.9987435,0.0001164119,0.0002800969,0.0003092695,0.000237395,0.0003133748],"domain_scores_gemma":[0.9992377,0.0001335645,0.0001029933,0.0003512374,0.00007964164,0.00009491249],"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":[0.0000411531,0.00003067881,0.0004713053,0.000002672328,0.000009576829,0.000005727041,0.00008266946,0.00003238433,0.9930144,0.0001445923,0.004391463,0.001773389],"study_design_scores_gemma":[0.00035849,0.00004487598,0.003772969,0.00002327407,0.00003314041,0.00002003323,0.00002685919,0.00003338334,0.9806776,0.0004101508,0.01446552,0.0001337128],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9918816,0.00008167946,0.002179605,0.004098702,0.0007213607,0.0001425057,0.0001070148,0.00006635922,0.0007211615],"genre_scores_gemma":[0.9916993,0.000009056719,0.00005579726,0.0001761355,0.0001973629,0.00003831635,0.000006159927,0.00001840076,0.007799445],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0123368,"threshold_uncertainty_score":0.9996886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04316057985268138,"score_gpt":0.2978462605122775,"score_spread":0.2546856806595961,"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."}}