{"id":"W2800694460","doi":"10.1080/15376516.2018.1449042","title":"Method to characterize inorganic particulates in lung tissue biopsies using field emission scanning electron microscopy","year":2018,"lang":"en","type":"article","venue":"Toxicology Mechanisms and Methods","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"U.S. Department of Defense","keywords":"Scanning electron microscope; Energy-dispersive X-ray spectroscopy; Particle (ecology); Filtration (mathematics); Particle size; Chemistry; Digestion (alchemy); Mineralogy; Characterization (materials science); Chemical engineering; Materials science; Analytical Chemistry (journal); Environmental chemistry; Chromatography; Nanotechnology; Biology; Composite material; Ecology","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002612618,0.0001689748,0.0003127401,0.00008354687,0.0002553457,0.00002622138,0.0001264,0.0002402538,0.000988415],"category_scores_gemma":[0.0002182445,0.0001545062,0.00001949397,0.000307756,0.00007316505,0.0001421893,0.0002317533,0.0001992485,0.0000217826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001568854,"about_ca_system_score_gemma":0.00003255305,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004566453,"about_ca_topic_score_gemma":0.00008569708,"domain_scores_codex":[0.9978725,0.0007346541,0.0003087728,0.0004006632,0.00009196259,0.0005915042],"domain_scores_gemma":[0.9991928,0.000273115,0.00009580183,0.0001629027,0.000009146196,0.0002662364],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006794656,0.00002808723,0.0006023723,0.00001793843,0.000003965845,0.000003143413,0.001576256,0.00001337465,0.9681403,0.0002550468,0.0001364817,0.02915507],"study_design_scores_gemma":[0.000218366,0.0007564081,0.008217924,0.0000359405,0.00001605506,0.0000289598,0.0001155564,0.004971831,0.979178,0.002970996,0.003302665,0.0001873237],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5677611,0.00004136172,0.4301563,0.001591957,0.0001225003,0.0002195705,0.0000010112,0.00002323413,0.00008297011],"genre_scores_gemma":[0.1227127,0.0000143217,0.8696824,0.007335894,0.00006216197,0.00001510927,0.000001164567,0.00001632138,0.0001599625],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4450485,"threshold_uncertainty_score":0.9999248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04541564105724114,"score_gpt":0.439341189016257,"score_spread":0.3939255479590159,"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."}}