{"id":"W4409036368","doi":"10.3749/9780921294788.ch11","title":"CHAPTER 11: USING BIOLOGICAL ARCHIVES TO DISCRIMINATE NATURAL FROM ANTHROPOGENIC MERCURY IN ANIMALS: A METHODOLOGICAL REVIEW","year":2005,"lang":"en","type":"review","venue":"Mineralogical Association of Canada eBooks","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Geological Survey of Canada","funders":"","keywords":"Mercury (programming language); Natural (archaeology); Environmental science; Geography; Archaeology; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"not_applicable","genre":"review","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"systematic_review","genre":"review","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002944943,0.0009903252,0.002231597,0.006166826,0.0004258277,0.00202317,0.001547611,0.001564573,0.002527851],"category_scores_gemma":[0.004382174,0.0005576215,0.0009808597,0.008630089,0.001004009,0.00197075,0.0009107883,0.0009181761,0.001457625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002158234,"about_ca_system_score_gemma":0.00612241,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01891118,"about_ca_topic_score_gemma":0.04111879,"domain_scores_codex":[0.9990632,0.0001709434,0.0001702825,0.0002128068,0.0003382895,0.00004452309],"domain_scores_gemma":[0.9969414,0.001571989,0.0002922738,0.0001039581,0.001048729,0.0000417087],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"systematic_review","study_design_scores_codex":[0.00005574034,0.00007118483,0.0009156352,0.03570773,0.000338945,0.000128849,0.0001585696,0.0002979095,0.003044353,0.002336945,0.02419588,0.9327483],"study_design_scores_gemma":[0.00002490791,0.0001413471,0.008726314,0.02959744,0.0009504393,0.0009416877,0.0003710188,0.0001426408,0.003522878,0.002567258,0.9529304,0.00008371467],"study_design_candidate":"systematic_review","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0002273821,0.9973637,0.0008115371,0.0003383487,0.0002522501,0.00003215974,0.0001085338,0.00001089652,0.0008552171],"genre_scores_gemma":[0.0008302295,0.995719,0.002008989,0.0002533343,0.0001216318,0.00002837171,0.0001119487,0.000006035583,0.0009204227],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.01891118,"threshold_uncertainty_score":0.03760219,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1550388192513357,"score_gpt":0.3745714198324486,"score_spread":0.2195326005811129,"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."}}