{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003420595,0.0004478697,0.0004249308,0.0001787324,0.0003149825,0.00048374,0.001121879,0.001099826,0.003642237],"category_scores_gemma":[0.0008291091,0.0002611271,0.0003537541,0.0003358684,0.0003836138,0.001149562,0.0004723227,0.001023553,0.0007040929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009026827,"about_ca_system_score_gemma":0.0008226106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01829506,"about_ca_topic_score_gemma":0.01596089,"domain_scores_codex":[0.9999076,0.00001355974,0.000004191431,0.00003444841,0.00002573101,0.00001449716],"domain_scores_gemma":[0.9998276,0.0000730774,0.00001346152,0.0000151179,0.00006222355,0.000008448567],"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":[0.00002917762,0.00002316451,0.0001674582,0.00002922998,0.000009595751,0.0000186558,0.00001404544,0.9732472,0.001537585,0.002954896,0.000660276,0.02130883],"study_design_scores_gemma":[9.225122e-7,0.000003746193,0.00002921365,8.975931e-7,0.000001148971,0.000001332521,7.095787e-7,0.9993067,0.0001447215,0.0004205896,0.0000890602,9.929832e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04252602,0.0003926251,0.9468794,0.0003467202,0.00009376433,0.00005633621,0.0002199856,0.0007116123,0.008773582],"genre_scores_gemma":[0.9245329,0.0002477961,0.05249979,0.0001006629,0.00004832667,0.0001350305,0.0002790849,0.00009979047,0.02205674],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01829506,"threshold_uncertainty_score":0.03637713,"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."}}