{"id":"W4407296790","doi":"10.3390/data10020024","title":"Visual Footprint of Separation Through Membrane Distillation on YouTube","year":2025,"lang":"en","type":"article","venue":"Data","topic":"Membrane Separation Technologies","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Footprint; Separation (statistics); Distillation; Chromatography; Computer science; Process engineering; Environmental science; Chemistry; Engineering; Geography; Machine learning; Archaeology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001426303,0.0004790072,0.000253451,0.002952586,0.0003819544,0.0004723771,0.0002212119,0.0003797694,0.003424079],"category_scores_gemma":[0.001053681,0.00006653114,0.0002307169,0.002161226,0.0001815886,0.0007521195,0.0005135931,0.0002956951,0.0007412654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004905393,"about_ca_system_score_gemma":0.0003519823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05024861,"about_ca_topic_score_gemma":0.09949724,"domain_scores_codex":[0.9998467,0.00002099289,0.000009186326,0.00002828924,0.00005534508,0.00003954754],"domain_scores_gemma":[0.9994848,0.0001944358,0.00007213305,0.00002750898,0.0001708279,0.00005035924],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002655548,0.0003958634,0.2163262,0.006045431,0.0003768583,0.007980472,0.01362943,0.008932737,0.06617577,0.006058554,0.3094384,0.3619847],"study_design_scores_gemma":[0.00004356608,0.0003014684,0.8314365,0.001002023,0.00008566509,0.001384726,0.01479263,0.03889972,0.01001131,0.001342658,0.100565,0.000134781],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8412017,0.002665982,0.002296182,0.001096983,0.0003327748,0.0001870915,0.1198146,0.0008574717,0.03154727],"genre_scores_gemma":[0.8853345,0.00203458,0.006155383,0.0003260181,0.0001751258,0.0002075962,0.09285355,0.0001998211,0.01271343],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05024861,"threshold_uncertainty_score":0.09991229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04202778023521123,"score_gpt":0.3574743070452022,"score_spread":0.315446526809991,"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."}}