{"id":"W4253890167","doi":"10.32920/ryerson.14647596.v1","title":"Alternating polystannanes: synthesis, preparation, characterization and a computational study","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Silicone and Siloxane Chemistry","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Tin; Substituent; Polymer; Polymerization; Condensation; Characterization (materials science); Materials science; Condensation reaction; Polymer chemistry; Combinatorial chemistry; Chemistry; Nanotechnology; Organic chemistry; Composite material; Catalysis; Physics; Thermodynamics","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":[0.0002103743,0.0004920766,0.0005425101,0.0002970511,0.0003403487,0.0004487513,0.0006140044,0.0006233702,0.003378192],"category_scores_gemma":[0.0004213369,0.0002851809,0.0003865145,0.0006332995,0.0002163144,0.0006347343,0.0001736949,0.000477598,0.0003042528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006116521,"about_ca_system_score_gemma":0.0005834764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003109087,"about_ca_topic_score_gemma":0.004138116,"domain_scores_codex":[0.99994,0.00000752702,0.00000308784,0.00001680895,0.00001756182,0.00001498884],"domain_scores_gemma":[0.9996905,0.0001871849,0.00004850821,0.00002351518,0.00003623554,0.00001402239],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003460514,0.0004770907,0.003806364,0.001259228,0.0000664667,0.000664662,0.00007306619,0.9313449,0.03705702,0.00713647,0.001374408,0.01639423],"study_design_scores_gemma":[0.00008701895,0.0004372178,0.001071816,0.00001390924,0.00003782085,0.0001181813,0.00006310179,0.9743004,0.02109268,0.0009801375,0.001780931,0.00001680693],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.987815,0.0008238832,0.00366795,0.0001371984,0.00001312118,0.00003405417,0.001205724,0.0000926288,0.006210456],"genre_scores_gemma":[0.9829026,0.001020192,0.01342307,0.00003021428,0.000008235118,0.00007035549,0.0009640849,0.00003175828,0.00154944],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003378192,"threshold_uncertainty_score":0.01130116,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01966738447589739,"score_gpt":0.2836943009141931,"score_spread":0.2640269164382957,"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."}}