{"id":"W4402981025","doi":"10.1109/otcon60325.2024.10688324","title":"Cutting-Edge Machine Learning Algorithms for In-Depth Characterization and Efficiency Improvement of Phase Change Heat Transfer Mechanisms","year":2024,"lang":"en","type":"article","venue":"","topic":"Laser and Thermal Forming Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Enhanced Data Rates for GSM Evolution; Computer science; Characterization (materials science); Algorithm; Phase (matter); Artificial intelligence; Machine learning; Materials science; Chemistry; Nanotechnology","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":[0.001133103,0.001326006,0.0008816842,0.001110674,0.0003216481,0.0008795472,0.001489916,0.001097675,0.001911436],"category_scores_gemma":[0.002708192,0.000496008,0.0008618828,0.0008055733,0.0005704786,0.001415836,0.0008894641,0.001669407,0.0006610073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006412367,"about_ca_system_score_gemma":0.0009641777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001697568,"about_ca_topic_score_gemma":0.002401306,"domain_scores_codex":[0.9997054,0.00005975948,0.00001981988,0.00007160212,0.0001174793,0.00002596377],"domain_scores_gemma":[0.9992797,0.0003725739,0.00008601791,0.00008392968,0.0001576402,0.00002024877],"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":[0.00006796054,0.0001170261,0.001396194,0.0003502702,0.00007593964,0.00005782077,0.00008662535,0.6394784,0.01032118,0.0129076,0.002004541,0.3331364],"study_design_scores_gemma":[0.000003353445,0.00001879173,0.00009397658,0.00001145188,0.000005585822,0.00001382875,0.000006020518,0.9923484,0.002466626,0.004322232,0.0007053993,0.000004272416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003880189,0.0003866931,0.9940805,0.0000792602,0.00001870645,0.00002675531,0.00003154422,0.000600768,0.000895619],"genre_scores_gemma":[0.2860372,0.0008450967,0.710132,0.0001940446,0.00005155181,0.0002953535,0.0003198092,0.0002547525,0.001870148],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001911436,"threshold_uncertainty_score":0.006394386,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01850168342948035,"score_gpt":0.2609610527490539,"score_spread":0.2424593693195735,"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."}}