{"id":"W4401386375","doi":"10.1016/j.apenergy.2024.124118","title":"A performance neural network model for conventional solar stills via transfer learning","year":2024,"lang":"en","type":"article","venue":"Applied Energy","topic":"Solar-Powered Water Purification Methods","field":"Energy","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Deanship of Scientific Research, King Saud University; King Abdulaziz University; Scientific Committee on Antarctic Research; DAISY Foundation; University of Northern British Columbia; Artificial Intelligence and Data Analytics Lab, Prince Sultan University; Department of Chemical Engineering, Monash University; King Saud bin Abdulaziz University for Health Science","keywords":"Artificial neural network; Transfer of learning; Distillation; Solar still; Generalization; Artificial intelligence; Machine learning; Hyperparameter; Computer science; Desalination; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000425107,0.0002566423,0.0002449494,0.0001002292,0.0002176714,0.00009449105,0.0002317657,0.0001781893,0.0001988859],"category_scores_gemma":[0.000008130439,0.0002534631,0.0001689636,0.0002760575,0.00005878898,0.0001523438,0.00003109145,0.0002524279,0.00005748714],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005780044,"about_ca_system_score_gemma":0.00005650381,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003516745,"about_ca_topic_score_gemma":0.00001631779,"domain_scores_codex":[0.9982862,0.0000658107,0.0003806895,0.0005152568,0.0002377395,0.0005142423],"domain_scores_gemma":[0.9993607,0.0001917348,0.00003038098,0.0002553019,0.00005376004,0.0001080565],"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":[0.00007471305,0.00001905953,0.0000159045,0.00004767359,0.00006751129,0.000001136431,0.0001753481,0.7469563,0.005992392,0.1775234,0.000502087,0.0686245],"study_design_scores_gemma":[0.0003800388,0.00003769962,0.00003950693,0.0000168868,0.00004420331,0.000006326277,0.00001021722,0.8647227,0.005879885,0.01016108,0.1184317,0.0002697381],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03234797,0.0004183774,0.9582936,0.0001224952,0.0005974017,0.0001651345,0.000008387773,0.0005328747,0.007513755],"genre_scores_gemma":[0.9705468,0.0000618236,0.02316373,0.0002946498,0.0006281716,0.0005086964,0.0002190264,0.0001090444,0.004467998],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9381989,"threshold_uncertainty_score":0.9999918,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02414263089114287,"score_gpt":0.2592031569326743,"score_spread":0.2350605260415314,"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."}}