{"id":"W3119814422","doi":"10.26434/chemrxiv.7638074.v1","title":"One-Pot Synthesis: Polymers, Hydrogel and Nanoparticles from Natural Extracted Green Materials","year":2019,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Chemistry and Chemical Engineering","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Dispersity; Nanomaterials; Polymer; Gel permeation chromatography; Nanoparticle; Polymerization; Emulsion polymerization; Context (archaeology); Green chemistry; Materials science; Nanotechnology; Chemical engineering; Chemistry; Organic chemistry; Polymer chemistry; Catalysis","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":[0.0000724067,0.0002793675,0.0003456641,0.00001044566,0.00003652281,0.0000604325,0.0003332063,0.0002959314,0.00273498],"category_scores_gemma":[0.00005438982,0.0002921706,0.00006536589,0.0000470388,0.0001252944,0.0000834266,0.0008277955,0.0003246957,0.0001584525],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008902121,"about_ca_system_score_gemma":0.000008704045,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004997597,"about_ca_topic_score_gemma":0.000004081659,"domain_scores_codex":[0.9986536,0.00001059172,0.0002545976,0.0005798173,0.0002102192,0.0002911868],"domain_scores_gemma":[0.9992123,0.000093884,0.0001072686,0.0004502742,0.000003506171,0.0001327313],"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":[0.00001824035,0.00003361635,0.0006061597,0.0000989687,0.00004390713,0.000004369953,0.00006589122,0.00005153036,0.9977564,0.000001420382,0.00007186345,0.001247675],"study_design_scores_gemma":[0.000126585,0.000002167292,0.004750285,0.0001524669,0.00006301073,0.000002644137,0.00001431351,0.0004096315,0.9936141,0.0003074045,0.0001921214,0.0003653056],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9978877,0.0004811082,0.000004274343,0.0001465224,0.0001882329,0.0001184507,0.00003058877,0.00009910059,0.001044022],"genre_scores_gemma":[0.9989094,0.00005024909,0.0002839246,0.00004821804,0.0001475469,0.00003300817,0.00006631327,0.00003200307,0.000429364],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004144125,"threshold_uncertainty_score":0.999953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008763976188466175,"score_gpt":0.1874799376899062,"score_spread":0.17871596150144,"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."}}