{"id":"W3092650220","doi":"10.7717/peerj.10082","title":"Using trace elements to identify the geographic origin of migratory bats","year":2020,"lang":"en","type":"article","venue":"PeerJ","topic":"Bat Biology and Ecology Studies","field":"Agricultural and Biological Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Maryland Center for Environmental Science","keywords":"Trace element; TRACE (psycholinguistics); Range (aeronautics); Environmental science; Geography; Ecology; Biology; Geology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001783775,0.00006669509,0.0001202492,0.000004595506,0.0001894788,0.000006752628,0.0001906132,0.00005139521,0.0002003577],"category_scores_gemma":[0.00006450106,0.00002074376,0.0000593447,0.0002221408,0.00008644917,0.00002716183,0.0000761679,0.00007290755,0.00003579992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003731669,"about_ca_system_score_gemma":0.000002847617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001187431,"about_ca_topic_score_gemma":0.0006739385,"domain_scores_codex":[0.9994315,0.00005392205,0.0001346718,0.0001491176,0.00006783765,0.0001629527],"domain_scores_gemma":[0.9997497,0.00009115061,0.00004898442,0.00002747232,0.00003498816,0.00004765897],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002361483,0.000028393,0.746305,0.000004385849,0.00004820071,0.000001734779,0.0003063546,0.000004770911,0.2448639,0.0001108107,0.004297909,0.00400492],"study_design_scores_gemma":[0.00004910332,0.0001267352,0.9655009,0.000002752087,0.00001811554,8.648802e-7,0.0004693674,0.00002084181,0.002285741,0.0001012218,0.03136009,0.00006428656],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9862185,0.0003656562,0.000001961537,0.0130147,0.0001130705,0.0001070538,0.00001795063,0.00001792053,0.0001431513],"genre_scores_gemma":[0.9977964,0.00002329265,0.00006449666,0.00195946,0.000108159,0.000006204768,0.00000330204,3.058003e-7,0.0000383575],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2425781,"threshold_uncertainty_score":0.2193777,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09188678237193626,"score_gpt":0.3090099705976001,"score_spread":0.2171231882256638,"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."}}