{"id":"W34798063","doi":"10.1016/j.stemcr.2021.10.011","title":"Adhesion of Wet Snow to Different Cable Surfaces","year":2009,"lang":"en","type":"article","venue":"Stem Cell Reports","topic":"Icing and De-icing Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of General Medical Sciences; National Heart, Lung, and Blood Institute; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; National Institute of Biomedical Imaging and Bioengineering; Électricité de France; Hydro-Québec; Université du Québec à Chicoutimi","keywords":"Snow; Adhesive; Materials science; Composite material; Ultimate tensile strength; Compressive strength; Shear strength (soil); Adhesion; Direct shear test; Shear (geology); Geotechnical engineering; Geology; Physics; Meteorology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006774124,0.0002469207,0.0001429562,0.0002163732,0.0002498623,0.0003462894,0.0001519375,0.0002425501,0.00286433],"category_scores_gemma":[0.000145603,0.0001125561,0.0002171544,0.0001747137,0.0001699354,0.0001874026,0.0003438491,0.0002585124,0.0003271751],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000265939,"about_ca_system_score_gemma":0.0001139595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009036345,"about_ca_topic_score_gemma":0.001721769,"domain_scores_codex":[0.9998862,0.000007270521,0.00000500972,0.00001916015,0.00003343407,0.00004902304],"domain_scores_gemma":[0.9999298,0.00001166483,0.00002110689,0.000008470076,0.000009510302,0.00001941227],"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":[0.000184941,0.00002101531,0.002327809,0.0000419897,0.0000118037,0.0001990319,0.00004632375,0.0003876558,0.9939604,0.0001182707,0.0001444916,0.002556264],"study_design_scores_gemma":[0.00001741676,0.0004153685,0.02282327,0.00001410469,0.00002111579,0.0003243323,0.000484817,0.004470398,0.967452,0.0001552227,0.003807612,0.00001425713],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971064,0.000148287,0.001102019,0.00003214105,0.00002902469,0.00001054997,0.0001137915,0.00001907437,0.001438763],"genre_scores_gemma":[0.9966024,0.0002225588,0.0008744494,0.00003846438,0.000007057219,0.00001133834,0.0002680591,0.00001255483,0.001963106],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00286433,"threshold_uncertainty_score":0.009582162,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009306702441267604,"score_gpt":0.201282896688364,"score_spread":0.1919761942470964,"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."}}