Application of the ECT9 protocol for radiocarbon-based sourceapportionment of carbonaceous aerosols
Bibliographic record
Abstract
Abstract. Carbonaceous aerosol is mainly composed of organic carbon (OC) and elemental carbon (EC). Both OC and EC originate from a variety of emission sources. Radiocarbon (14C) analysis can be used to apportion bulk aerosol, OC, and EC into their sources. However, such analyses require the physical separation of OC and EC. Here, we apply of ECT9 protocol to physically isolate OC and EC for 14C analysis and evaluate its effectiveness. Several reference materials are selected, including: two pure OC (fossil adipic acid, contemporary sucrose), two pure EC (fossil regal black and C1150), and three complex materials containing contemporary and/or fossil OC and EC (rice char and NIST urban dust standards SRM1649a, i.e., bulk dust and SRM8785, i.e., fine fraction of re-suspended SRM1649a on filter). The pure materials were measured for their OC, EC and total carbon (TC) mass fractions and corresponding carbon isotopes to evaluate the uncertainty of the procedure. The average accuracy of TC mass, determined via volumetric injection of a sucrose solution, was approximately 5 %. Ratios of EC/TC and OC/TC were highly reproducible, with analytical precisions better than 2 % for all reference materials, ranging in size from 20 to 100 µg C. Consensus values were reached for all pure reference materials for both δ13C and FM14C with an uncertainty of
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.012 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".