155 A test Chamber to Quantify Emission Factors for Welding Fumes
Bibliographic record
Abstract
Abstract A conical test chamber was built to quantify emission factors for various types of welding processes. The chamber was built according to American Welding Society (AWS) standard F1.2:2013. Fumes were collected on pre-dried and pre-weighted 293 mm glass fiber filters using a high-volume sampling pump running at approximately 30 cfm. Consumables were weighted before and after welding to calculate the amount of fume emitted per weight of consumable. All the welding was performed by a professional welder. The chamber was calibrated prior to any use to adjust the flowrate inside the chamber. The test chamber was used to calculate emission factors for Shielded Metal Arc Welding process using four different electrodes (E6013, E6011, E7018, E7014) and three different currents for each electrode as fume emission increases with current. Each test was done in triplicates to investigate for variation. Emission factors were calculated by diving the amount of fume on the filters by the weight of consumables used (g/kg). In general test were reproducible with coefficient of variations varying from 0.2 to 18.1% with most variation below 15%. Emission factors were highly dependent on the type of electrode with the lowest values found for E6013 and the highest for E6011. Emission factors increased significantly with increasing current. The chamber works properly and can be used to test other processes such as Gas Metal Arc Welding, Metal Core Arc Welding, Flux Core Arc Welding, as well as Gas Tungsten Arc Welding. Emission factors will also be calculated for metals.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".