{"id":"W4363673167","doi":"10.22541/au.168114067.71599553/v1","title":"Triacontane and Behenyl Lignocerate Molecular Tilting in Crystals: Theory, Monte Carlo Simulations and Predictions","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Food Chemistry and Fat Analysis","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; St. Francis Xavier University","funders":"St. Francis Xavier University; University of Guelph; Utah Agricultural Experiment Station; Natural Sciences and Engineering Research Council of Canada; Utah State University","keywords":"Molecule; Monte Carlo method; Tilt (camera); Monolayer; Work (physics); Materials science; Plane (geometry); Statistical physics; Crystallography; Physics; Chemical physics; Chemistry; Computational chemistry; Molecular physics; Nanotechnology; Thermodynamics; Mathematics; Quantum mechanics; Geometry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006058897,0.0004477109,0.000874784,0.0007395339,0.0007009526,0.0009563353,0.001181309,0.001664,0.002084723],"category_scores_gemma":[0.001683214,0.000522665,0.0009719933,0.0006814051,0.001149982,0.001071631,0.0004890859,0.000924382,0.0003000503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001044936,"about_ca_system_score_gemma":0.001119075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01249399,"about_ca_topic_score_gemma":0.006829179,"domain_scores_codex":[0.9998423,0.00004961369,0.000006749156,0.00001813797,0.00004460126,0.00003851115],"domain_scores_gemma":[0.9992166,0.0004654141,0.00006593906,0.00005242421,0.0001303291,0.00006924143],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003876522,0.00004586175,0.00109486,0.0001564776,0.0000331352,0.0001115832,0.0000883206,0.9335868,0.001521139,0.05963959,0.0005958796,0.003087424],"study_design_scores_gemma":[0.00001090209,0.000009103174,0.0001832697,0.00001177638,0.000005632434,0.000008815034,0.00001127332,0.9892423,0.0002301538,0.009856039,0.0004236366,0.000007145218],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6699784,0.007230392,0.2706641,0.002596043,0.0005203753,0.0002149647,0.001156671,0.0005466032,0.04709245],"genre_scores_gemma":[0.9399055,0.004930718,0.04602284,0.0005220902,0.0001866891,0.0006191271,0.0008459447,0.0002739412,0.006693141],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01249399,"threshold_uncertainty_score":0.02484256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0286323072509252,"score_gpt":0.2447950300030492,"score_spread":0.216162722752124,"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."}}