{"id":"W1991370819","doi":"10.1016/j.jinorgbio.2008.09.008","title":"Molybdenum X-ray absorption edges from 200 to 20,000 eV: The benefits of soft X-ray spectroscopy for chemical speciation","year":2008,"lang":"en","type":"article","venue":"Journal of Inorganic Biochemistry","topic":"X-ray Spectroscopy and Fluorescence Analysis","field":"Physics and Astronomy","cited_by":51,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of General Medical Sciences","keywords":"Chemistry; Molybdenum; K-edge; XANES; Analytical Chemistry (journal); Absorption (acoustics); Chemical shift; X-ray spectroscopy; Measure (data warehouse); Spectroscopy; Spectral line; Absorption edge; Absorption spectroscopy; Laser linewidth; Crystallography; Optics; Physics; Condensed matter physics; Inorganic chemistry; Physical chemistry; Band gap","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":[0.0003789589,0.0004840947,0.0004197197,0.0006313117,0.0004222756,0.001422368,0.0005594701,0.0007484019,0.002978657],"category_scores_gemma":[0.0005173762,0.0004845677,0.0002082332,0.0005227253,0.0005155543,0.00248809,0.00067858,0.001424263,0.0006982237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003513783,"about_ca_system_score_gemma":0.0002552241,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003577041,"about_ca_topic_score_gemma":0.001023457,"domain_scores_codex":[0.9998821,0.00002038916,0.000006914255,0.00003012661,0.00003952945,0.00002087152],"domain_scores_gemma":[0.9998456,0.00005900163,0.00002654906,0.00002290886,0.00002751841,0.00001831327],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0004729082,0.00003844183,0.001739025,0.0002578047,0.00001814519,0.0001065156,0.0001154951,0.0003246211,0.9518785,0.00598177,0.0006744301,0.03839224],"study_design_scores_gemma":[0.00003949646,0.0001653124,0.00638442,0.000109515,0.00006746246,0.0005885289,0.0001637186,0.00608871,0.952085,0.01336808,0.02090829,0.0000315285],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8399827,0.0443824,0.08113579,0.005712113,0.0004069368,0.00004751622,0.0007869762,0.001560923,0.02598462],"genre_scores_gemma":[0.9113498,0.01468383,0.06079745,0.001100544,0.0001028364,0.00003517056,0.0005128374,0.0003479579,0.01106956],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002978657,"threshold_uncertainty_score":0.009964585,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009245028949491382,"score_gpt":0.2307076359524706,"score_spread":0.2214626070029792,"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."}}