Metal-free sampling methods for dust, rainwater, surface water, plants, and sediments: A selection of unique tools from the SWAMP laboratory
Bibliographic record
Abstract
Contamination control remains one of the greatest challenges for the reliable determination of many trace elements in environmental samples. Here we describe a series of metal-free sampling devices and tools designed and constructed specifically to minimize the risk of contamination by trace elements during sampling of dust, rainwater, surface water, plants, and sediments. Plastic components fabricated using 3-D printing include polylactic acid (PLA), polyethylene terephthalate (PET), polyethylene terephthalate glycol (PETG), polypropylene (PP), polycarbonate (PC) and PC with carbon fibre. When additional strength is needed (e.g. supporting structural components), carbon fibre, aluminum (Al), or 316 stainless steel (SS) is used. Other plastics employed include acrylic and vinyl. Epoxy glue or SS may be used for joining components, but do not come into contact with the samples. Ceramic (zirconium dioxide) cutting blades are used where needed. Each plastic material was evaluated for contaminant trace elements by leaching with high purity nitric acid in the metal-free, ultraclean SWAMP laboratory. The devices were tested in the field to evaluate their performance and durability. When combined with appropriate cleaning procedures, the equipment enables ultraclean collection for trace element analysis of environmental media.•Plastic sampling devices were designed and constructed using 3D printing of PLA, PET, PETG or PP.•Leaching characteristics of plastic components were evaluated using high purity nitric acid in a metal-free, ultraclean laboratory.•Each sampling device was successfully field-tested in industrial settings (near open pit bitumen mines and upgraders), and in remote locations of northern Alberta, Canada.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".