{"id":"W612500909","doi":"","title":"Bring on the snow","year":2014,"lang":"en","type":"article","venue":"Airports international","topic":"Smart Materials for Construction","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Snow; Transport engineering; Aeronautics; Engineering; Meteorology; Geography; Civil engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001879961,0.00004633656,0.00003020054,0.00001201278,0.00005538899,0.00002893181,0.0001453953,0.00001587042,0.006975919],"category_scores_gemma":[0.00008541976,0.00003268847,0.00002265541,0.00002417078,0.00006567912,0.00007932039,0.00007509122,0.00003837201,0.001856547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005197554,"about_ca_system_score_gemma":0.000001491812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009505121,"about_ca_topic_score_gemma":0.00003947713,"domain_scores_codex":[0.9994907,0.00001616391,0.00008404972,0.0001159288,0.0002221578,0.00007097812],"domain_scores_gemma":[0.9997587,0.00004514965,0.00004690185,0.0001278507,0.000003630901,0.0000177816],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001923562,0.00004301071,0.8463309,7.870121e-7,0.00002126476,0.000004749164,0.0001149024,0.001129932,0.005353441,0.06549654,0.05146048,0.03002469],"study_design_scores_gemma":[0.0001081383,0.00002160909,0.6889814,0.00001132292,0.00000295735,0.00003128757,0.00001190004,0.001543098,0.007326982,0.00784912,0.2940161,0.00009617634],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8714512,2.024484e-7,0.0003619249,0.001651021,0.001463548,0.00003932132,9.80083e-7,0.00002581122,0.125006],"genre_scores_gemma":[0.9982074,6.210809e-7,0.0001798953,0.0007260231,0.0002420863,0.00000512629,0.000003994137,0.00000498907,0.0006298296],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2425556,"threshold_uncertainty_score":0.9989206,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007166865221695222,"score_gpt":0.1997132450035173,"score_spread":0.1925463797818221,"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."}}