Metal-free sampling methods for dust, rainwater, surface water, plants, and sediments: A selection of unique tools from the SWAMP laboratory
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it