{"id":"W4399137786","doi":"10.1002/sia.7337","title":"Surface science insight note: Imaging X‐ray photoelectron spectroscopy","year":2024,"lang":"en","type":"article","venue":"Surface and Interface Analysis","topic":"Electron and X-Ray Spectroscopy Techniques","field":"Materials Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Basic Energy Sciences; Office of Science; Centre National de la Recherche Scientifique; Canadian Nautical Research Society; Engineering and Physical Sciences Research Council; U.S. Department of Energy","keywords":"X-ray photoelectron spectroscopy; Computer science; Data set; Materials science; Chemistry; Biological system; Analytical Chemistry (journal); Artificial intelligence; Physics; Nuclear magnetic resonance; Environmental chemistry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0006443365,0.0006724221,0.0002800489,0.001033358,0.0003934381,0.00127707,0.000745885,0.001110316,0.02757578],"category_scores_gemma":[0.001052786,0.0003551262,0.0002665112,0.000674634,0.0003314675,0.001068901,0.0006968845,0.001431166,0.01080734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005678613,"about_ca_system_score_gemma":0.0006630632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008567525,"about_ca_topic_score_gemma":0.002146913,"domain_scores_codex":[0.999333,0.00005125418,0.00001934503,0.00005404204,0.0004980201,0.00004443428],"domain_scores_gemma":[0.9993489,0.000146646,0.0000480613,0.00007467793,0.000325926,0.00005576437],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002662344,0.0001510042,0.001013141,0.0006387211,0.00002054119,0.001051319,0.0001525888,0.0007769734,0.263352,0.02520775,0.4190668,0.2883029],"study_design_scores_gemma":[0.00003174315,0.0002614568,0.003067178,0.0001127262,0.00001301106,0.002892164,0.00009466702,0.004497547,0.09964696,0.009069432,0.880269,0.00004408088],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04514598,0.02953693,0.4737483,0.04313589,0.01748668,0.001244863,0.01023176,0.01486536,0.3646041],"genre_scores_gemma":[0.1488019,0.02684006,0.3085806,0.006383243,0.003704537,0.0004046529,0.008364033,0.002132971,0.4947881],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02757578,"threshold_uncertainty_score":0.09225023,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008004017064034037,"score_gpt":0.3032883401136657,"score_spread":0.2952843230496317,"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."}}