{"id":"W2194116046","doi":"10.1016/j.scitotenv.2015.05.004","title":"Using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) to characterize copper, zinc and mercury along grizzly bear hair providing estimate of diet","year":2015,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":50,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria; Stantec (Canada)","funders":"Division of Ocean Sciences; National Research Council Canada; Raincoast Conservation Foundation; Oregon State University; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Washington State University","keywords":"Zinc; Grizzly Bears; Inductively coupled plasma mass spectrometry; Cadmium; Mercury (programming language); Chemistry; Trace element; Copper; Laser ablation; Analytical Chemistry (journal); Environmental chemistry; Mass spectrometry; Laser; Chromatography; Ursus","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":[0.0002270341,0.0003118627,0.0001762144,0.0005640357,0.0007828291,0.0003173372,0.0002570393,0.0004168977,0.001222881],"category_scores_gemma":[0.0002853846,0.0001944791,0.0002845133,0.0004187426,0.0003867453,0.0001978089,0.000230757,0.0003561409,0.0003997621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002346035,"about_ca_system_score_gemma":0.0004029968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01510788,"about_ca_topic_score_gemma":0.03687963,"domain_scores_codex":[0.9998264,0.00002044156,0.000005502396,0.00006344068,0.0000669023,0.00001744916],"domain_scores_gemma":[0.9998696,0.00001858838,0.00002149879,0.00001176716,0.00006738359,0.00001125824],"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":[0.0002566837,0.00007818564,0.07901364,0.00006660095,0.00008133602,0.0001599772,0.0006486269,0.0001315199,0.902506,0.000174539,0.0002843911,0.01659844],"study_design_scores_gemma":[0.00003372804,0.001491621,0.5038124,0.00002387055,0.0002601368,0.00171407,0.00104998,0.002829325,0.4804616,0.0004640568,0.007825055,0.00003420434],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9742339,0.0003338169,0.0207907,0.0001230562,0.00004634104,0.00007756432,0.0007271444,0.0001251382,0.003542296],"genre_scores_gemma":[0.9543388,0.000417142,0.03069624,0.0002416074,0.00002125575,0.00006512091,0.0003759629,0.0000488592,0.0137949],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01510788,"threshold_uncertainty_score":0.03003985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0362815627510894,"score_gpt":0.2638796324908752,"score_spread":0.2275980697397858,"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."}}