{"id":"W4396243363","doi":"10.5194/egusphere-2024-752","title":"Aggregation of ice-nucleating macromolecules from Betula pendula pollen determines ice nucleation efficiency","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Turfgrass Adaptation and Management","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Technische Universität Wien Bibliothek; Österreichische Forschungsförderungsgesellschaft","keywords":"Ice nucleus; Nucleation; Frost (temperature); Supercooling; Betula pendula; Chemical physics; Atmospheric sciences; Chemistry; Botany; Materials science; Geology; Biology; Meteorology; Physics; Composite material","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":[0.000198544,0.0002799984,0.0002619288,0.0001123122,0.00008827758,0.0001292523,0.0003999322,0.0001606238,0.002937368],"category_scores_gemma":[0.00006037532,0.000259654,0.0001512874,0.0002037454,0.0001035101,0.0001010928,0.001684667,0.0002305546,0.0004352046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001764657,"about_ca_system_score_gemma":0.00001629852,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00609349,"about_ca_topic_score_gemma":0.001112482,"domain_scores_codex":[0.9979765,0.00006969727,0.0005187563,0.0006699604,0.0005545551,0.0002105475],"domain_scores_gemma":[0.9990681,0.00007412795,0.0003336795,0.0004329907,0.00001962805,0.00007147211],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001112124,0.0007240726,0.007098641,0.00112888,0.0003912828,0.00009772117,0.01290989,0.116922,0.104059,0.01351075,0.01037703,0.7326695],"study_design_scores_gemma":[0.001770538,0.0003487068,0.1332615,0.001911997,0.00101737,0.000007400627,0.007459971,0.7391295,0.0397019,0.05631139,0.01610153,0.002978176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9180595,0.0001546525,0.008523869,0.0005398575,0.0006467049,0.0006738749,0.00009991308,0.0002259613,0.07107562],"genre_scores_gemma":[0.9863248,0.00005075563,0.01118572,0.000137979,0.00006466748,0.00002936315,0.0001707163,0.00004010642,0.001995857],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7296913,"threshold_uncertainty_score":0.9999856,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01259429548834983,"score_gpt":0.2389605864186981,"score_spread":0.2263662909303483,"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."}}